paper,repo,found
cross-lingual language model pretraining,https://github.com/facebookresearch/XLM,Y
cross-lingual language model pretraining,https://github.com/facebookresearch/UnsupervisedMT,Y
cross-lingual language model pretraining,https://github.com/tianxin1860/Papers,N
cross-lingual language model pretraining,https://github.com/Somefive/XNLI,Y
cross-lingual language model pretraining,https://github.com/xutaatmicrosoftdotcom/MASS,N
unsupervised neural machine translation with smt as posterior regularization,https://github.com/Imagist-Shuo/UNMT-SPR,N
phrase-based & neural unsupervised machine translation,https://github.com/facebookresearch/XLM,Y
phrase-based & neural unsupervised machine translation,https://github.com/facebookresearch/UnsupervisedMT,Y
phrase-based & neural unsupervised machine translation,https://github.com/xutaatmicrosoftdotcom/MASS,N
phrase-based & neural unsupervised machine translation,https://github.com/Helsinki-NLP/shared-info,Y
unsupervised statistical machine translation,https://github.com/artetxem/vecmap,N
unsupervised statistical machine translation,https://github.com/artetxem/monoses,Y
edinburgh neural machine translation systems for wmt 16,https://github.com/rsennrich/wmt16-scripts,N
understanding back-translation at scale,https://github.com/pytorch/fairseq,N
understanding back-translation at scale,https://github.com/facebookresearch/fairseq-py,N
scaling neural machine translation,https://github.com/pytorch/fairseq,N
scaling neural machine translation,https://github.com/facebookresearch/fairseq-py,N
pay less attention with lightweight and dynamic convolutions,https://github.com/pytorch/fairseq,N
self-attention with relative position representations,https://github.com/tensorflow/tensor2tensor,Y
self-attention with relative position representations,https://github.com/mawright/attn_rl,Y
weighted transformer network for machine translation,https://github.com/xrick/PyTorch_Transformer,Y
weighted transformer network for machine translation,https://github.com/JayParks/transformer,Y
weighted transformer network for machine translation,https://github.com/duyvuleo/Transformer-DyNet,Y
the evolved transformer,https://github.com/tensorflow/tensor2tensor,Y
convolutional sequence to sequence learning,https://github.com/stefannatu/NLP-Translation-Models,Y
convolutional sequence to sequence learning,https://github.com/oneTimePad/conv-nmt,Y
convolutional sequence to sequence learning,https://github.com/CKPOON0619/Kaggle2Sigma,Y
convolutional sequence to sequence learning,https://github.com/phanideepgampa/IM2LATEX,Y
convolutional sequence to sequence learning,https://github.com/facebookresearch/fairseq,Y
convolutional sequence to sequence learning,https://github.com/Kakoedlinnoeslovo/fairseq,Y
convolutional sequence to sequence learning,https://github.com/Izecson/saml-nmt,N
convolutional sequence to sequence learning,https://github.com/facebookresearch/ParlAI,N
convolutional sequence to sequence learning,https://github.com/Deeksha96/Im2Latex,Y
convolutional sequence to sequence learning,https://github.com/somnath-banerjee/deep,Y
convolutional sequence to sequence learning,https://github.com/facebookresearch/fairseq-py,N
convolutional sequence to sequence learning,https://github.com/Izecson/sockeye-1.16.6,Y
convolutional sequence to sequence learning,https://github.com/awslabs/sockeye,Y
convolutional sequence to sequence learning,https://github.com/shashiongithub/XSum,Y
attention is all you need,https://github.com/shuo-git/THUMT-UNCERTAINTY,Y
attention is all you need,https://github.com/yueyongjiao/attention_is_all_you_need-GPU,Y
attention is all you need,https://github.com/zysite/post,N
attention is all you need,https://github.com/asfathermou/human-computer-interaction,Y
attention is all you need,https://github.com/shuaishuaij/Machine-Translation,Y
attention is all you need,https://github.com/guoyaohua/BERT-Chinese-Annotation,Y
attention is all you need,https://github.com/awslabs/sockeye,Y
attention is all you need,https://github.com/TSLNIHAOGIT/bert,Y
attention is all you need,https://github.com/Laura1021678398/CCF-Bert,Y
attention is all you need,https://github.com/nathananderson94/PortugueseMT,Y
attention is all you need,https://github.com/SYangDong/bert-with-frozen-code,Y
attention is all you need,https://github.com/Nstats/bert_MRC,Y
attention is all you need,https://github.com/Shasvat-Desai/Shasvat-Desai,Y
attention is all you need,https://github.com/Satan012/BERT,Y
attention is all you need,https://github.com/antoinecollas/transformer_neural_machine_translation,Y
attention is all you need,https://github.com/semal/bert,Y
attention is all you need,https://github.com/guillaume-chevalier/Linear-Attention-Recurrent-Neural-Network,Y
attention is all you need,https://github.com/xiaopingzhong/bert-finetune-for-classfier,Y
attention is all you need,https://github.com/natel9178/transformer-refork,Y
attention is all you need,https://github.com/ZmeiGorynych/transformer_pytorch,Y
attention is all you need,https://github.com/svakulenk0/response_eval,Y
attention is all you need,https://github.com/socc-io/piqaboo,Y
attention is all you need,https://github.com/yumoh/speech-keras,Y
attention is all you need,https://github.com/mayurnewase/Quora-Bert,Y
attention is all you need,https://github.com/MichaelZhouwang/LMlexsub,Y
attention is all you need,https://github.com/mawright/attn_rl,Y
attention is all you need,https://github.com/omallo/kaggle-hpa,Y
attention is all you need,https://github.com/rpuiggari/bert2,Y
attention is all you need,https://github.com/dreamnotover/english_chinese_machine_translation_baseline,Y
attention is all you need,https://github.com/mayurnewase/Translation,Y
attention is all you need,https://github.com/jageshmaharjan/BERT_Service,N
attention is all you need,https://github.com/woonzh/semantics,Y
attention is all you need,https://github.com/akikaaa/Transformer,N
attention is all you need,https://github.com/mflotynski/ninja-bert,Y
attention is all you need,https://github.com/threelittlemonkeys/transformer-pytorch,Y
attention is all you need,https://github.com/gittb/audiosandbox,Y
attention is all you need,https://github.com/phohenecker/pytorch-transformer,Y
attention is all you need,https://github.com/WenYanger/General-Transformer-Pytorch,Y
attention is all you need,https://github.com/jbdel/OMG_UMONS_submission,Y
attention is all you need,https://github.com/jsalt18-sentence-repl/jiant,N
attention is all you need,https://github.com/LoveYang/bert_test,N
attention is all you need,https://github.com/wayalhruhi/gogle_bert,Y
attention is all you need,https://github.com/TSLNIHAOGIT/bert_run,Y
attention is all you need,https://github.com/goldenbili/Bert_Test3,Y
attention is all you need,https://github.com/GlassyWing/transformer-keras,Y
attention is all you need,https://github.com/benjamintaiwo/Attention,Y
attention is all you need,https://github.com/dmlc/gluon-nlp,N
attention is all you need,https://github.com/IrishCoffee/cudnnMultiHeadAttention,Y
attention is all you need,https://github.com/dreamgonfly/Transformer-pytorch,Y
attention is all you need,https://github.com/Izecson/sockeye-1.16.6,Y
attention is all you need,https://github.com/lovedavidsilva/bert_old_version,Y
attention is all you need,https://github.com/zhongyunuestc/bert_multitask,Y
attention is all you need,https://github.com/shaikhzhas/bert,Y
attention is all you need,https://github.com/derylucio/Transformer,Y
attention is all you need,https://github.com/icewing1996/bert_dep,Y
attention is all you need,https://github.com/yanqi1811/attention-is-all-you-need,Y
attention is all you need,https://github.com/zapplea/bert,Y
attention is all you need,https://github.com/Colanim/BERT_STS-B,Y
attention is all you need,https://github.com/zsweet/BERT_zsw,Y
attention is all you need,https://github.com/graykode/nlp-tutorial,Y
attention is all you need,https://github.com/longbowking/bert,Y
attention is all you need,https://github.com/ethancaballero/neural-engineers-first-attempt,Y
attention is all you need,https://github.com/Helsinki-NLP/shared-info,Y
attention is all you need,https://github.com/freedombenLiu/bert,Y
attention is all you need,https://github.com/nmfisher/bert-modified,Y
attention is all you need,https://github.com/kingcheng2000/bert,Y
attention is all you need,https://github.com/Doffery/BERT-Sentiment-Analysis-Amazon-Review,N
attention is all you need,https://github.com/saurabhgupta17888/BERT,N
attention is all you need,https://github.com/jean-kunz/ml_research_papers,Y
attention is all you need,https://github.com/HKUST-KnowComp/R-Net,Y
attention is all you need,https://github.com/soskek/attention_is_all_you_need,Y
attention is all you need,https://github.com/saurabhnlp/bert,Y
attention is all you need,https://github.com/tcxdgit/MachineTranslation_t2t,Y
attention is all you need,https://github.com/dl-nlp/dl-nlp.github.io,Y
attention is all you need,https://github.com/tjrwlgns1198/ChatBot,N
attention is all you need,https://github.com/anoidgit/transformer,Y
attention is all you need,https://github.com/pomonam/AttentionCluster,Y
attention is all you need,https://github.com/yydai/bert_test,Y
attention is all you need,https://github.com/brandontrabucco/scaled_dot_product_attention,Y
attention is all you need,https://github.com/yueyongjiao/Transformer-Contextual,Y
attention is all you need,https://github.com/jgcbrouns/Introducing-Sparsity-in-the-Transformer,Y
attention is all you need,https://github.com/tyxr/bert,Y
attention is all you need,https://github.com/jadore801120/attention-is-all-you-need-pytorch,Y
attention is all you need,https://github.com/audier/DeepSpeechRecognition,Y
attention is all you need,https://github.com/arvieFrydenlund/bert,Y
attention is all you need,https://github.com/facebookresearch/fairseq-py,N
attention is all you need,https://github.com/guzhang480/Google_BERT,Y
attention is all you need,https://github.com/gxdalu-yaya/bert,Y
attention is all you need,https://github.com/layziezz/tesuto,Y
attention is all you need,https://github.com/xbtlin/All-about-Machine-Learning,Y
attention is all you need,https://github.com/gychant/attention-is-all-you-need-pytorch,N
attention is all you need,https://github.com/nietao2/DeepSpeechRecognition,N
attention is all you need,https://github.com/chen-xiong-yi/OwnBERT,Y
attention is all you need,https://github.com/XUJitao/WordEmbedding,Y
attention is all you need,https://github.com/DandyQi/bert-learning,N
attention is all you need,https://github.com/Izecson/saml-nmt,N
attention is all you need,https://github.com/Belval/NRTR,N
attention is all you need,https://github.com/myamamoto555/tf-bert,N
attention is all you need,https://github.com/kaituoxu/Speech-Transformer,Y
attention is all you need,https://github.com/insigh/THUMT,Y
attention is all you need,https://github.com/Glaceon31/Document-Transformer,N
attention is all you need,https://github.com/tensorflow/tensor2tensor,Y
attention is all you need,https://github.com/Kyubyong/transformer,Y
attention is all you need,https://github.com/yueyongjiao/attention_is_all_you_need_pytorch,Y
attention is all you need,https://github.com/MaZhiyuanBUAA/bert-tf1.4.0,Y
attention is all you need,https://github.com/tcnguyen/bert,Y
attention is all you need,https://github.com/chuanbanjun/bert_comment,Y
attention is all you need,https://github.com/MarcBS/keras,Y
attention is all you need,https://github.com/Lsdefine/attention-is-all-you-need-keras,Y
attention is all you need,https://github.com/vijay120/bert,Y
attention is all you need,https://github.com/YYGXjpg/BERT_WL,Y
attention is all you need,https://github.com/liqifyl/google-bert,N
attention is all you need,https://github.com/FlorianPfisterer/2D-LSTM-Seq2Seq,Y
attention is all you need,https://github.com/yiyc-kor/bert-study,Y
attention is all you need,https://github.com/felix-do-wizardry/bert_felix,Y
attention is all you need,https://github.com/tangmingyi/BERT,N
attention is all you need,https://github.com/Neoanarika/torchexplainer,Y
attention is all you need,https://github.com/NVIDIA/sentiment-discovery,Y
attention is all you need,https://github.com/kolloldas/torchnlp,Y
attention is all you need,https://github.com/goldenbili/Bert_Test2,Y
attention is all you need,https://github.com/thumt/THUMT,Y
attention is all you need,https://github.com/XapaJIaMnu/transformative,Y
attention is all you need,https://github.com/JohannLee1996/bert,Y
attention is all you need,https://github.com/coxy1989/tfmr,Y
attention is all you need,https://github.com/luckynozomi/PPI_Bert,Y
attention is all you need,https://github.com/huoyanfeilikesi/GOOGLE-BERT,Y
attention is all you need,https://github.com/xesdiny/test-bert-master,Y
attention is all you need,https://github.com/DeokO/bert-excercise-ongoing,Y
attention is all you need,https://github.com/ShuvenduBikash/transformer_spelling_corrector,Y
attention is all you need,https://github.com/Gaozhen0816/BERT_QA_For_AILaw,Y
attention is all you need,https://github.com/NathanDuran/BERT,Y
attention is all you need,https://github.com/saurabhkulkarni77/BERT_multilabel,Y
attention is all you need,https://github.com/frankcgq105/BERTCHEN,Y
attention is all you need,https://github.com/pengming617/bert_classification,N
attention is all you need,https://github.com/fciannel/bert_fciannel,Y
attention is all you need,https://github.com/Kevin-Vora/bert-embedding-gluonnlp-edit-,Y
attention is all you need,https://github.com/pytorch/fairseq,N
attention is all you need,https://github.com/THUNLP-MT/THUMT,Y
attention is all you need,https://github.com/brainsqueeze/text2vec,Y
attention is all you need,https://github.com/adi2103/AML-CoVe,Y
attention is all you need,https://github.com/Nstats/my_bert,Y
attention is all you need,https://github.com/DevSinghSachan/multilingual_nmt,N
attention is all you need,https://github.com/why2000/DuReader-bert,Y
attention is all you need,https://github.com/PrideLee/sentiment-analysis,Y
attention is all you need,https://github.com/jangjoongkeon/JK,N
attention is all you need,https://github.com/kotu931226/classifier_transformer_pytorch,Y
attention is all you need,https://github.com/simonjisu/annotated-transformer-kr,Y
attention is all you need,https://github.com/brightmart/bert_customized,Y
attention is all you need,https://github.com/chiayewken/bert-qa,Y
google's neural machine translation system: bridging the gap between human and machine translation,https://github.com/ThAIKeras/bert,N
google's neural machine translation system: bridging the gap between human and machine translation,https://github.com/ChaiBapchya/mxnet-hardmax_machine_translation,N
google's neural machine translation system: bridging the gap between human and machine translation,https://github.com/Nirvanabc/seq2seq-rnn,N
google's neural machine translation system: bridging the gap between human and machine translation,https://github.com/google/sentencepiece,N
google's neural machine translation system: bridging the gap between human and machine translation,https://github.com/dmlc/gluon-nlp,N
google's neural machine translation system: bridging the gap between human and machine translation,https://github.com/nkjsy/Neural-Machine-Translation,N
google's neural machine translation system: bridging the gap between human and machine translation,https://github.com/guillaume-chevalier/HAR-stacked-residual-bidir-LSTMs,N
google's neural machine translation system: bridging the gap between human and machine translation,https://github.com/Automattic/wp-translate,N
google's neural machine translation system: bridging the gap between human and machine translation,https://github.com/ZHANG45/multi_gpu_seq2seq,N
google's neural machine translation system: bridging the gap between human and machine translation,https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition,N
google's neural machine translation system: bridging the gap between human and machine translation,https://github.com/macco3k/deepstories,N
addressing the rare word problem in neural machine translation,https://github.com/sunnysinghnitb/text_corrector_software,N
addressing the rare word problem in neural machine translation,https://github.com/sunnysinghnitb/text-corrector-software,N
addressing the rare word problem in neural machine translation,https://github.com/atpaino/deep-text-corrector,N
addressing the rare word problem in neural machine translation,https://github.com/gongshuangshuang/deep-text-corrector,N
addressing the rare word problem in neural machine translation,https://github.com/sambit9238/deep_text_corrector,N
sequence to sequence learning with neural networks,https://github.com/giovanniguidi/Seq-2-Seq-OCR,Y
sequence to sequence learning with neural networks,https://github.com/littleflow3r/Sequence_to_sequence_learning_with_NN,Y
sequence to sequence learning with neural networks,https://github.com/ArushiSinghal/Neural-Machine-Translation-English-Hindi-for-domain-data,Y
sequence to sequence learning with neural networks,https://github.com/VishalFun/Keras_practice,Y
sequence to sequence learning with neural networks,https://github.com/matken11235/keras-seq2seq,Y
sequence to sequence learning with neural networks,https://github.com/mlennox/summarisers,Y
sequence to sequence learning with neural networks,https://github.com/weiylu/NLP,Y
sequence to sequence learning with neural networks,https://github.com/littleflow3r/SeqtoSeq_learning_for_machine_translation,Y
sequence to sequence learning with neural networks,https://github.com/damtharvey/wug-test,Y
sequence to sequence learning with neural networks,https://github.com/TEAMLAB-Lecture/deep_nlp_101,Y
sequence to sequence learning with neural networks,https://github.com/ahmadelsallab/spell_corrector,Y
sequence to sequence learning with neural networks,https://github.com/kirillermolov/ChatBot,Y
sequence to sequence learning with neural networks,https://github.com/astorfi/sequence-to-sequence-from-scratch,Y
sequence to sequence learning with neural networks,https://github.com/littleflow3r/Japanese_English_Machine_Translation_Seq2Seq,Y
sequence to sequence learning with neural networks,https://github.com/ArushiSinghal/NMT-assignment1,Y
sequence to sequence learning with neural networks,https://github.com/littleflow3r/Sequence_to_sequence_learning_for_machine_translation,Y
sequence to sequence learning with neural networks,https://github.com/ArushiSinghal/Neural-Machine-Translation,Y
sequence to sequence learning with neural networks,https://github.com/farizrahman4u/seq2seq,Y
sequence to sequence learning with neural networks,https://github.com/isi-nlp/Zoph_RNN,Y
sequence to sequence learning with neural networks,https://github.com/nouhadziri/THRED,Y
sequence to sequence learning with neural networks,https://github.com/astorfi/neural-machine-translation-from-scratch,Y
neural machine translation by jointly learning to align and translate,https://github.com/jiangnanhugo/seq2seq_cuda,Y
neural machine translation by jointly learning to align and translate,https://github.com/shuo-git/THUMT-UNCERTAINTY,Y
neural machine translation by jointly learning to align and translate,https://github.com/Epoch-Mengying/Generating-Poetry-with-Chatbot,Y
neural machine translation by jointly learning to align and translate,https://github.com/eske/seq2seq,Y
neural machine translation by jointly learning to align and translate,https://github.com/Matthewdowney18/Yelp_seq2seq,Y
neural machine translation by jointly learning to align and translate,https://github.com/sunnysinghnitb/text-corrector-software,N
neural machine translation by jointly learning to align and translate,https://github.com/brightmart/text_classification,Y
neural machine translation by jointly learning to align and translate,https://github.com/b-etienne/Seq2seq-PyTorch,Y
neural machine translation by jointly learning to align and translate,https://github.com/Izecson/sockeye-1.16.6,Y
neural machine translation by jointly learning to align and translate,https://github.com/awslabs/sockeye,Y
neural machine translation by jointly learning to align and translate,https://github.com/qq345736500/sarcasm,Y
neural machine translation by jointly learning to align and translate,https://github.com/slme1109/lyrics-generator,Y
neural machine translation by jointly learning to align and translate,https://github.com/thunlp/TensorFlow-Summarization,Y
neural machine translation by jointly learning to align and translate,https://github.com/thumt/THUMT,Y
neural machine translation by jointly learning to align and translate,https://github.com/atpaino/deep-text-corrector,Y
neural machine translation by jointly learning to align and translate,https://github.com/DCYN/Ramdomized-Clinical-Trail-Classification,Y
neural machine translation by jointly learning to align and translate,https://github.com/Izecson/saml-nmt,N
neural machine translation by jointly learning to align and translate,https://github.com/YvesWang/Machine_Translation_NLP,N
neural machine translation by jointly learning to align and translate,https://github.com/SimonDele/Glossary,Y
neural machine translation by jointly learning to align and translate,https://github.com/ScientiaEtVeritas/NeuralMachineTranslation,Y
neural machine translation by jointly learning to align and translate,https://github.com/graykode/nlp-tutorial,Y
neural machine translation by jointly learning to align and translate,https://github.com/lkfo415579/MT-Readling-List,Y
neural machine translation by jointly learning to align and translate,https://github.com/sambit9238/deep_text_corrector,Y
neural machine translation by jointly learning to align and translate,https://github.com/datalogue/keras-attention,Y
neural machine translation by jointly learning to align and translate,https://github.com/mp2893/gram,Y
neural machine translation by jointly learning to align and translate,https://github.com/thomlake/pytorch-attention,Y
neural machine translation by jointly learning to align and translate,https://github.com/A-Jacobson/minimal-nmt,Y
neural machine translation by jointly learning to align and translate,https://github.com/insigh/THUMT,Y
neural machine translation by jointly learning to align and translate,https://github.com/Glaceon31/Document-Transformer,N
neural machine translation by jointly learning to align and translate,https://github.com/abhishekr7/Summarization-of-Radiological-Reports,N
neural machine translation by jointly learning to align and translate,https://github.com/VeritasAuthorship/veritas,Y
neural machine translation by jointly learning to align and translate,https://github.com/brainsqueeze/text2vec,Y
neural machine translation by jointly learning to align and translate,https://github.com/THUNLP-MT/THUMT,Y
neural machine translation by jointly learning to align and translate,https://github.com/Baichenjia/NMT-eager,Y
neural machine translation by jointly learning to align and translate,https://github.com/mayurnewase/Translation,Y
neural machine translation by jointly learning to align and translate,https://github.com/astorfi/sequence-to-sequence-from-scratch,Y
neural machine translation by jointly learning to align and translate,https://github.com/Matthewdowney18/Yelp_attention,Y
neural machine translation by jointly learning to align and translate,https://github.com/SwordYork/DCNMT,Y
neural machine translation by jointly learning to align and translate,https://github.com/sunnysinghnitb/text_corrector_software,N
neural machine translation by jointly learning to align and translate,https://github.com/farizrahman4u/seq2seq,N
neural machine translation by jointly learning to align and translate,https://github.com/simonjisu/NMT,Y
neural machine translation by jointly learning to align and translate,https://github.com/labdac/charlacompling,Y
neural machine translation by jointly learning to align and translate,https://github.com/gongshuangshuang/deep-text-corrector,N
neural machine translation by jointly learning to align and translate,https://github.com/astorfi/neural-machine-translation-from-scratch,Y
neural machine translation by jointly learning to align and translate,https://github.com/umutguneri/Question-Answering-Assistant,Y
neural machine translation by jointly learning to align and translate,https://github.com/sumanbanerjee1/Code-Mixed-Dialog,Y
a convolutional encoder model for neural machine translation,https://github.com/somnath-banerjee/deep,Y
a convolutional encoder model for neural machine translation,https://github.com/facebookresearch/fairseq,Y
learning phrase representations using rnn encoder-decoder for statistical machine translation,https://github.com/littleflow3r/Sequence_to_sequence_learning_with_NN,N
learning phrase representations using rnn encoder-decoder for statistical machine translation,https://github.com/littleflow3r/Japanese_English_Machine_Translation_Seq2Seq,N
learning phrase representations using rnn encoder-decoder for statistical machine translation,https://github.com/mp2893/gram,N
learning phrase representations using rnn encoder-decoder for statistical machine translation,https://github.com/littleflow3r/Sequence_to_sequence_learning_for_machine_translation,N
learning phrase representations using rnn encoder-decoder for statistical machine translation,https://github.com/CongBao/ChatBot,N
learning phrase representations using rnn encoder-decoder for statistical machine translation,https://github.com/spratskevich/Lemmatizer,N
learning phrase representations using rnn encoder-decoder for statistical machine translation,https://github.com/farizrahman4u/seq2seq,N
learning phrase representations using rnn encoder-decoder for statistical machine translation,https://github.com/littleflow3r/SeqtoSeq_learning_for_machine_translation,N
learning phrase representations using rnn encoder-decoder for statistical machine translation,https://github.com/damtharvey/wug-test,N
learning phrase representations using rnn encoder-decoder for statistical machine translation,https://github.com/astorfi/neural-machine-translation-from-scratch,N
learning phrase representations using rnn encoder-decoder for statistical machine translation,https://github.com/astorfi/sequence-to-sequence-from-scratch,N
learning phrase representations using rnn encoder-decoder for statistical machine translation,https://github.com/graykode/nlp-tutorial,N
learning phrase representations using rnn encoder-decoder for statistical machine translation,https://github.com/trevor-richardson/rnn_zoo,N
learning phrase representations using rnn encoder-decoder for statistical machine translation,https://github.com/erickrf/autoencoder,N
learning phrase representations using rnn encoder-decoder for statistical machine translation,https://github.com/Avmb/lowrank-gru,N
recurrent neural network regularization,https://github.com/Hapiny/tensorflow-rnn-shakespeare,Y
recurrent neural network regularization,https://github.com/rgarzonj/LSTMs,Y
recurrent neural network regularization,https://github.com/saschaschramm/LSTM,Y
recurrent neural network regularization,https://github.com/BenjaminGonzalez/BamCode,Y
recurrent neural network regularization,https://github.com/tmatha/lstm,Y
recurrent neural network regularization,https://github.com/sebastianGehrmann/tensorflow-statereader,Y
recurrent neural network regularization,https://github.com/floydhub/word-language-model,Y
recurrent neural network regularization,https://github.com/martin-gorner/tensorflow-rnn-shakespeare,Y
recurrent neural network regularization,https://github.com/isi-nlp/Zoph_RNN,Y
recurrent neural network regularization,https://github.com/wojzaremba/lstm,N
recurrent neural network regularization,https://github.com/hikaruya8/lstm_model_py,Y
recurrent neural network regularization,https://github.com/nbansal90/bAbi_QA,Y
can active memory replace attention?,https://github.com/tensorflow/models/tree/master/research/neural_gpu,Y
neural machine translation in linear time,https://github.com/adityaagrawal7/speech-to-text-wavenet,Y
neural machine translation in linear time,https://github.com/Stephenzwang/lipreader,Y
neural machine translation in linear time,https://github.com/kinimod23/ATS_Project,Y
neural machine translation in linear time,https://github.com/kingstarcraft/speech-to-text-wavenet2,Y
neural machine translation in linear time,https://github.com/freedombenLiu/speech-to-text-wavenet,Y
neural machine translation in linear time,https://github.com/liguigui/speech-to-text-wavenet,Y
neural machine translation in linear time,https://github.com/paarthneekhara/byteNet-tensorflow,Y
neural machine translation in linear time,https://github.com/katyalnikita/Optimize_Query,N
unsupervised neural machine translation,https://github.com/artetxem/undreamt,Y
unsupervised neural machine translation,https://github.com/rsennrich/subword-nmt,N
the best of both worlds: combining recent advances in neural machine translation,https://github.com/zysite/post,N
the best of both worlds: combining recent advances in neural machine translation,https://github.com/duyvuleo/Transformer-DyNet,N
universal transformers,https://github.com/GlassyWing/transformer-word-segmenter,Y
universal transformers,https://github.com/tensorflow/tensor2tensor,Y
universal transformers,https://github.com/MostafaDehghani/bAbI-T2T,Y
universal transformers,https://github.com/cfiken/universal_transformer,Y
universal transformers,https://github.com/kienduynguyen/Adaptive-Computation,N
universal transformers,https://github.com/akikaaa/Transformer,N
non-autoregressive neural machine translation,https://github.com/MultiPath/NA-NMT,Y
non-autoregressive neural machine translation,https://github.com/salesforce/nonauto-nmt,Y
deterministic non-autoregressive neural sequence modeling by iterative refinement,https://github.com/nyu-dl/dl4mt-nonauto,N
sequence-level knowledge distillation,https://github.com/harvardnlp/seq2seq-attn,Y
sequence-level knowledge distillation,https://github.com/harvardnlp/nmt-android,Y
pervasive attention: 2d convolutional neural networks for sequence-to-sequence prediction,https://github.com/poifull10/PervasiveAttentionNet,N
pervasive attention: 2d convolutional neural networks for sequence-to-sequence prediction,https://github.com/elbayadm/attn2d,N
pervasive attention: 2d convolutional neural networks for sequence-to-sequence prediction,https://github.com/tdiggelm/nn-experiments,N
frage: frequency-agnostic word representation,https://github.com/ChengyueGongR/FrequencyAgnostic,Y
frage: frequency-agnostic word representation,https://github.com/bifeng/deep_coding_notes,N
latent alignment and variational attention,https://github.com/harvardnlp/var-attn,Y
classical structured prediction losses for sequence to sequence learning,https://github.com/pytorch/fairseq,N
towards neural phrase-based machine translation,https://github.com/Microsoft/NPMT,Y
towards neural phrase-based machine translation,https://github.com/posenhuang/NPMT,Y
an actor-critic algorithm for sequence prediction,https://github.com/rizar/actor-critic-public,N
an actor-critic algorithm for sequence prediction,https://github.com/akashkm99/RL-Paper-Notes,Y
compressing word embeddings via deep compositional code learning,https://github.com/zomux/neuralcompressor,Y
sequence-to-sequence learning as beam-search optimization,https://github.com/pytorch/fairseq,N
sequence-to-sequence learning as beam-search optimization,https://github.com/facebookresearch/fairseq-py,N
sequence level training with recurrent neural networks,https://github.com/NPCai/Nopie,Y
sequence level training with recurrent neural networks,https://github.com/eske/seq2seq,Y
sequence level training with recurrent neural networks,https://github.com/CZWin32768/seqmnist,Y
sequence level training with recurrent neural networks,https://github.com/facebookresearch/MIXER,Y
quasi-recurrent neural networks,https://github.com/salesforce/pytorch-qrnn,Y
quasi-recurrent neural networks,https://github.com/Kyubyong/quasi-rnn,Y
quasi-recurrent neural networks,https://github.com/jcd12/biomind,Y
quasi-recurrent neural networks,https://github.com/zhou059/w266-project,Y
neural machine translation of rare words with subword units,https://github.com/nyu-dl/dl4mt-cdec,Y
neural machine translation of rare words with subword units,https://github.com/EdinburghNLP/code-docstring-corpus,Y
neural machine translation of rare words with subword units,https://github.com/Automattic/wp-translate,Y
neural machine translation of rare words with subword units,https://github.com/ThAIKeras/bert,Y
neural machine translation of rare words with subword units,https://github.com/facebookresearch/fairseq,Y
neural machine translation of rare words with subword units,https://github.com/SeonbeomKim/Python-Byte_Pair_Encoding,Y
neural machine translation of rare words with subword units,https://github.com/ksulima/Unsupervised-method-to-NPL-Polish-language,Y
neural machine translation of rare words with subword units,https://github.com/SeonbeomKim/Python-Bype_Pair_Encoding,Y
neural machine translation of rare words with subword units,https://github.com/nyu-dl/dl4mt-c2c,Y
neural machine translation of rare words with subword units,https://github.com/simonjisu/NMT,Y
neural machine translation of rare words with subword units,https://github.com/somnath-banerjee/deep,Y
neural machine translation of rare words with subword units,https://github.com/lkfo415579/MT-Readling-List,Y
neural machine translation of rare words with subword units,https://github.com/facebookresearch/fairseq-py,N
neural machine translation of rare words with subword units,https://github.com/Avmb/code-docstring-corpus,Y
neural machine translation of rare words with subword units,https://github.com/glample/fastBPE,Y
linguistic input features improve neural machine translation,https://github.com/rsennrich/wmt16-scripts,N
unsupervised neural machine translation with weight sharing,https://github.com/ZhenYangIACAS/unsupervised-NMT,N
unsupervised machine translation using monolingual corpora only,https://github.com/jiajunhua/facebookresearch-MUSE,Y
unsupervised machine translation using monolingual corpora only,https://github.com/facebookresearch/MUSE,Y
unsupervised machine translation using monolingual corpora only,https://github.com/maochf/MUSE,Y
unsupervised machine translation using monolingual corpora only,https://github.com/samnguyen8991/Facebook-MUSE,Y
unsupervised machine translation using monolingual corpora only,https://github.com/labdac/charlacompling,Y
unsupervised machine translation using monolingual corpora only,https://github.com/freedombenLiu/MUSE,Y
unsupervised machine translation using monolingual corpora only,https://github.com/migonch/unsupervised_mt,Y
unsupervised machine translation using monolingual corpora only,https://github.com/YovaKem/generalized-procrustes-MUSE,Y
a character-level decoder without explicit segmentation for neural machine translation,https://github.com/nyu-dl/dl4mt-cdec,N
a character-level decoder without explicit segmentation for neural machine translation,https://github.com/nyu-dl/dl4mt-c2c,N
simple recurrent units for highly parallelizable recurrence,https://github.com/Lukasz-G/Hydra,N
simple recurrent units for highly parallelizable recurrence,https://github.com/suzhiba/SimpleRecurrentUnits-SRU-,Y
simple recurrent units for highly parallelizable recurrence,https://github.com/ra1nty/sru-naive,Y
simple recurrent units for highly parallelizable recurrence,https://github.com/sangjeedondrub/my-awesome-list,Y
simple recurrent units for highly parallelizable recurrence,https://github.com/seppilee/Papers,N
simple recurrent units for highly parallelizable recurrence,https://github.com/taolei87/sru,Y
depthwise separable convolutions for neural machine translation,https://github.com/tensorflow/tensor2tensor,Y
depthwise separable convolutions for neural machine translation,https://github.com/outcastofmusic/quick-nlp,Y
neural semantic encoders,https://github.com/saraswat/munkhdalai-nse,Y
neural semantic encoders,https://github.com/Smerity/keras_snli,Y
convolutional 2d knowledge graph embeddings,https://github.com/TimDettmers/ConvE,Y
tucker: tensor factorization for knowledge graph completion,https://github.com/ibalazevic/TuckER,Y
hypernetwork knowledge graph embeddings,https://github.com/ibalazevic/HypER,Y
canonical tensor decomposition for knowledge base completion,https://github.com/facebookresearch/kbc,Y
simple embedding for link prediction in knowledge graphs,https://github.com/Mehran-k/SimplE,N
analogical inference for multi-relational embeddings,https://github.com/quark0/ANALOGY,Y
embedding entities and relations for learning and inference in knowledge bases,https://github.com/facebookresearch/PyTorch-BigGraph,N
predicting semantic relations using global graph properties,https://github.com/yuvalpinter/m3gm,N
multi-task graph autoencoders,https://github.com/vuptran/graph-representation-learning,Y
variational graph auto-encoders,https://github.com/tkipf/gae,Y
esrgan: enhanced super-resolution generative adversarial networks,https://github.com/xinntao/ESRGAN,Y
esrgan: enhanced super-resolution generative adversarial networks,https://github.com/fenghansen/ESRGAN-Keras,Y
esrgan: enhanced super-resolution generative adversarial networks,https://github.com/xinntao/BasicSR,N
a fully progressive approach to single-image super-resolution,https://github.com/singnet/super-resolution-service,N
a fully progressive approach to single-image super-resolution,https://github.com/fperazzi/proSR,Y
image super-resolution using very deep residual channel attention networks,https://github.com/ChaofWang/AWSRN,N
image super-resolution using very deep residual channel attention networks,https://github.com/yulunzhang/RCAN,Y
image super-resolution using very deep residual channel attention networks,https://github.com/shesay-noway/Super-Resolution-RCAN,Y
enhanced deep residual networks for single image super-resolution,https://github.com/shimo8810/NTIRE2017,Y
enhanced deep residual networks for single image super-resolution,https://github.com/thstkdgus35/EDSR-PyTorch,Y
enhanced deep residual networks for single image super-resolution,https://github.com/krasserm/wdsr,Y
enhanced deep residual networks for single image super-resolution,https://github.com/markmaxt/VideoSR,Y
enhanced deep residual networks for single image super-resolution,https://github.com/jamesgolden1/mri_super_resolution,Y
enhanced deep residual networks for single image super-resolution,https://github.com/LimBee/NTIRE2017,Y
residual dense network for image super-resolution,https://github.com/puffnjackie/pytorch-super-resolution-implementations,N
residual dense network for image super-resolution,https://github.com/yulunzhang/RDN,Y
feedback network for image super-resolution,https://github.com/Paper99/SRFBN_CVPR19,Y
beyond deep residual learning for image restoration: persistent homology-guided manifold simplification,https://github.com/iorism/CNN,N
multi-level wavelet-cnn for image restoration,https://github.com/lpj0/MWCNN,N
"fast, accurate, and lightweight super-resolution with cascading residual network",https://github.com/nmhkahn/CARN-pytorch,N
ram: residual attention module for single image super-resolution,https://github.com/S-aiueo32/SRRAM,Y
lightweight and efficient image super-resolution with block state-based recursive network,https://github.com/idearibosome/tf-bsrn-sr,N
non-local recurrent network for image restoration,https://github.com/Ding-Liu/NLRN,Y
learning a single convolutional super-resolution network for multiple degradations,https://github.com/cszn/SRMD,Y
enhancenet: single image super-resolution through automated texture synthesis,https://github.com/geonm/EnhanceNet-Tensorflow,N
enhancenet: single image super-resolution through automated texture synthesis,https://github.com/msmsajjadi/EnhanceNet-Code,N
enhancenet: single image super-resolution through automated texture synthesis,https://github.com/advaza/enhancenet_pretrained,N
memnet: a persistent memory network for image restoration,https://github.com/tyshiwo/MemNet,N
sesr: single image super resolution with recursive squeeze and excitation networks,https://github.com/opteroncx/SESR,N
fast and accurate single image super-resolution via information distillation network,https://github.com/Zheng222/IDN-Caffe,Y
beyond a gaussian denoiser: residual learning of deep cnn for image denoising,https://github.com/shibuiwilliam/DeepLearningDenoise,N
beyond a gaussian denoiser: residual learning of deep cnn for image denoising,https://github.com/lye0618/NODE-Denoiser,N
beyond a gaussian denoiser: residual learning of deep cnn for image denoising,https://github.com/kfallah/NODE-Denoiser,N
beyond a gaussian denoiser: residual learning of deep cnn for image denoising,https://github.com/cszn/DnCNN,N
beyond a gaussian denoiser: residual learning of deep cnn for image denoising,https://github.com/ShakedDovrat/JpegArtifactRemoval,N
beyond a gaussian denoiser: residual learning of deep cnn for image denoising,https://github.com/tum-vision/learn_prox_ops,N
image super-resolution via dual-state recurrent networks,https://github.com/WeiHan3/dsrn,Y
image super-resolution using deep convolutional networks,https://github.com/fourseaforfriend/waifu2x,Y
image super-resolution using deep convolutional networks,https://github.com/07Agarg/Image-Resolution-Enhancement-SRCNN,Y
image super-resolution using deep convolutional networks,https://github.com/r06922019/butt_lion_paper_notes,Y
image super-resolution using deep convolutional networks,https://github.com/mayank-17/Project-Image-Restoration-using-SRCNN,Y
image super-resolution using deep convolutional networks,https://github.com/Shritesh99/Image_Super_Resolution,Y
image super-resolution using deep convolutional networks,https://github.com/nagadomi/waifu2x,Y
image super-resolution using deep convolutional networks,https://github.com/Pulkitdzrt/ML-Image-Super-Resolution,Y
image super-resolution using deep convolutional networks,https://github.com/titu1994/Image-Super-Resolution,N
image super-resolution using deep convolutional networks,https://github.com/soham239/MLND_Capstone_Project,Y
image super-resolution using deep convolutional networks,https://github.com/shreeyashyende/better_img_res_with_SRCNN,Y
image super-resolution using deep convolutional networks,https://github.com/Shritesh99/100DaysofMLCodeChallenge,Y
image super-resolution using deep convolutional networks,https://github.com/jupiterman/Super-Resolution-Images,N
image super-resolution using deep convolutional networks,https://github.com/HighVoltageRocknRoll/sr,Y
image super-resolution using deep convolutional networks,https://github.com/WarrenGreen/srcnn,Y
deep back-projection networks for super-resolution,https://github.com/thstkdgus35/EDSR-PyTorch,Y
deep back-projection networks for super-resolution,https://github.com/rajatthepagal/DBPN-Keras,Y
deep back-projection networks for super-resolution,https://github.com/15517965908/keras-DBPN,Y
deep back-projection networks for super-resolution,https://github.com/alterzero/DBPN-Pytorch,N
deep back-projection networks for super-resolution,https://github.com/markmaxt/VideoSR,Y
image reconstruction with predictive filter flow,https://github.com/aimerykong/predictive-filter-flow,Y
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/gongenhao/GANCS,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/dimawork1981/super_resolution_bot,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/jacquelinelala/GFN,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/ReNginx/FR-SRGAN,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/itsuki8914/srresnet4x,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/deepak112/Keras-SRGAN,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/idearibosome/tf-perceptual-eusr,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/leftthomas/SRGAN,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/JasonPlawinski/SuperResolution,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/jupiterman/Super-Resolution-Images,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/zijundeng/SRGAN,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/SDBurt/SRGAN-PT,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/CreativeCodingLab/DeepIllumination,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/jini7652/srgan002,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/anujtyagi2802/Keras-SRGAN,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/IBM/MAX-Image-Super-Resolution-Generator,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/captain-pool/SuperRes-GAN,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/goldhuang/SRGAN-PyTorch,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/sanju-27/Video-Super-Resolution,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/xinntao/BasicSR,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/zsdonghao/SRGAN,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/tensorlayer/SRGAN,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/krasserm/wdsr,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/brade31919/SRGAN-tensorflow,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/titu1994/Image-Super-Resolution,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/swordgeek/SR,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/IBM/MAX-Image-Resolution-Enhancer,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/zeerandhawa/Photo-Realistic-Image-Super-Resolution,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/anujtyagi2802/SRGAN,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/titu1994/Super-Resolution-using-Generative-Adversarial-Networks,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/leehomyc/Photo-Realistic-Super-Resoluton,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/twtygqyy/pytorch-SRResNet,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/junhocho/SRGAN,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/tensorflow/models/tree/master/research/gan,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/imatge-upc/3D-GAN-superresolution,N
photo-realistic single image super-resolution using a generative adversarial network,https://github.com/buriburisuri/SRGAN,N
"""zero-shot"" super-resolution using deep internal learning",https://github.com/assafshocher/ZSSR,Y
"""zero-shot"" super-resolution using deep internal learning",https://github.com/HarukiYqM/pytorch-ZSSR,Y
seven ways to improve example-based single image super resolution,https://github.com/jiny2001/dcscn-super-resolution,N
single image super-resolution with dilated convolution based multi-scale information learning inception module,https://github.com/wzhshi/MSSRNet,N
real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,https://github.com/KeremTurgutlu/papers,N
real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,https://github.com/imatge-upc/3D-GAN-superresolution,N
real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,https://github.com/tetrachrome/subpixel,N
real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,https://github.com/linxi159/GAN-training-tricks,N
real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,https://github.com/michael13162/DoodleGAN,N
real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,https://github.com/545641826/espcn,N
real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,https://github.com/kingcheng2000/GAN,N
real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,https://github.com/deepak112/Keras-SRGAN,N
real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,https://github.com/jaingaurav3/GAN-Hacks,N
real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,https://github.com/jiny2001/dcscn-super-resolution,N
real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,https://github.com/HighVoltageRocknRoll/sr,N
real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,https://github.com/linxi159/Tips-and-tricks-to-train-GANs,N
deep learning-based image super-resolution considering quantitative and perceptual quality,https://github.com/idearibosome/tf-perceptual-eusr,N
detail-revealing deep video super-resolution,https://github.com/jiangsutx/SPMC_VideoSR,Y
image super-resolution via feature-augmented random forest,https://github.com/HarleyHK/FARF,N
joint maximum purity forest with application to image super-resolution,https://github.com/HarleyHK/JMPF,Y
deep mean-shift priors for image restoration,https://github.com/siavashbigdeli/DMSP,N
recovering realistic texture in image super-resolution by deep spatial feature transform,https://github.com/xinntao/BasicSR,N
image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections,https://github.com/ifnspaml/Enhancement-Coded-Speech,N
perceptual losses for real-time style transfer and super-resolution,https://github.com/zhy1001/Fast_Style_Transfer_using_CNN,N
perceptual losses for real-time style transfer and super-resolution,https://github.com/seanmullery/Super-Resolution,Y
perceptual losses for real-time style transfer and super-resolution,https://github.com/Akella17/Voice_Style_Transfer,Y
perceptual losses for real-time style transfer and super-resolution,https://github.com/harunshimanto/Neural-Style-Transfer-of-Artistic-Style,Y
perceptual losses for real-time style transfer and super-resolution,https://github.com/etttttte/mayfest2018,Y
perceptual losses for real-time style transfer and super-resolution,https://github.com/anujdutt9/Artistic-Style-Transfer-using-Keras-Tensorflow,Y
perceptual losses for real-time style transfer and super-resolution,https://github.com/raahatg21/Creating-Artistic-Images,Y
perceptual losses for real-time style transfer and super-resolution,https://github.com/pmm09c/ntire-dehazing,Y
perceptual losses for real-time style transfer and super-resolution,https://github.com/abhiskk/fast-neural-style,Y
perceptual losses for real-time style transfer and super-resolution,https://github.com/aryan-mann/style-transfer,Y
perceptual losses for real-time style transfer and super-resolution,https://github.com/chrismgeorge/Artistic_Additions_To_Style_Transfer,Y
perceptual losses for real-time style transfer and super-resolution,https://github.com/RicCu/NeuralStyle,Y
perceptual losses for real-time style transfer and super-resolution,https://github.com/lxy5513/Multi-Style-Transfer,Y
perceptual losses for real-time style transfer and super-resolution,https://github.com/thatbrguy/Dehaze-GAN,Y
perceptual losses for real-time style transfer and super-resolution,https://github.com/AloneGu/keras_tf_neural_img,Y
perceptual losses for real-time style transfer and super-resolution,https://github.com/Apogentus/style-transfer-with-strength-control,N
perceptual losses for real-time style transfer and super-resolution,https://github.com/zhanghang1989/PyTorch-Multi-Style-Transfer,Y
perceptual losses for real-time style transfer and super-resolution,https://github.com/DmitryUlyanov/texture_nets,Y
perceptual losses for real-time style transfer and super-resolution,https://github.com/ryanchankh/style_transfer,Y
perceptual losses for real-time style transfer and super-resolution,https://github.com/DanielJabbour/StyleTransfer,Y
perceptual losses for real-time style transfer and super-resolution,https://github.com/noufali/VideoML,Y
perceptual losses for real-time style transfer and super-resolution,https://github.com/zhanghang1989/MSG-Net,Y
perceptual losses for real-time style transfer and super-resolution,https://github.com/chuanli11/MGANs,Y
neural nearest neighbors networks,https://github.com/visinf/n3net,Y
deep video super-resolution network using dynamic upsampling filters without explicit motion compensation,https://github.com/yhjo09/VSR-DUF,N
recurrent back-projection network for video super-resolution,https://github.com/alterzero/RBPN-PyTorch,N
frame-recurrent video super-resolution,https://github.com/ReNginx/FR-SRGAN,Y
learning for video super-resolution through hr optical flow estimation,https://github.com/LongguangWang/SOF-VSR-Super-Resolving-Optical-Flow-for-Video-Super-Resolution-,N
learning for video super-resolution through hr optical flow estimation,https://github.com/LongguangWang/SOF-VSR,N
real-time video super-resolution with spatio-temporal networks and motion compensation,https://github.com/LoSealL/VideoSuperResolution,N
"fast, accurate and lightweight super-resolution with neural architecture search",https://github.com/falsr/FALSR,N
image-to-image translation with conditional adversarial networks,https://github.com/opendot/ml4a-invisible-cities,N
image-to-image translation with conditional adversarial networks,https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix,Y
image-to-image translation with conditional adversarial networks,https://github.com/MarvinLavechin/imagetranslation-tensorflow,Y
image-to-image translation with conditional adversarial networks,https://github.com/XJhaoren/Academic-Group,Y
image-to-image translation with conditional adversarial networks,https://github.com/Montia/bw2color,Y
image-to-image translation with conditional adversarial networks,https://github.com/hariv/pix2pix-tensorflow,Y
image-to-image translation with conditional adversarial networks,https://github.com/leemathew1998/RG,Y
image-to-image translation with conditional adversarial networks,https://github.com/hariv/pix2pix,Y
image-to-image translation with conditional adversarial networks,https://github.com/tensorpack/tensorpack,N
image-to-image translation with conditional adversarial networks,https://github.com/gerardoglz/GANs,N
image-to-image translation with conditional adversarial networks,https://github.com/tbullmann/imagesegmentation-tensorflow,N
image-to-image translation with conditional adversarial networks,https://github.com/CreativeCodingLab/DeepIllumination,Y
image-to-image translation with conditional adversarial networks,https://github.com/linxi159/GAN-training-tricks,N
image-to-image translation with conditional adversarial networks,https://github.com/tbullmann/imagetranslation-tensorflow,Y
image-to-image translation with conditional adversarial networks,https://github.com/zuobinxiong/pix2pix-tensorflow,Y
image-to-image translation with conditional adversarial networks,https://github.com/chez8990/Image2ImageTranslation,Y
image-to-image translation with conditional adversarial networks,https://github.com/nishantcoder97/cgandemo,Y
image-to-image translation with conditional adversarial networks,https://github.com/jini7652/srgan002,N
image-to-image translation with conditional adversarial networks,https://github.com/philip-brohan/weather2weather,Y
image-to-image translation with conditional adversarial networks,https://github.com/a-martyn/unet,Y
image-to-image translation with conditional adversarial networks,https://github.com/andrewowens/multisensory,Y
image-to-image translation with conditional adversarial networks,https://github.com/yenchenlin/pix2pix-tensorflow,N
image-to-image translation with conditional adversarial networks,https://github.com/svikramank/pix2pix,Y
image-to-image translation with conditional adversarial networks,https://github.com/GAN-Challenger/pytorch-CycleGAN-and-pix2pix,Y
image-to-image translation with conditional adversarial networks,https://github.com/bionanoimaging/cellSTORM-Tensorflow,N
image-to-image translation with conditional adversarial networks,https://github.com/random-quark/pix2pix-tensorflow,Y
image-to-image translation with conditional adversarial networks,https://github.com/r06922019/butt_lion_paper_notes,Y
image-to-image translation with conditional adversarial networks,https://github.com/yd8534976/conGAN,Y
image-to-image translation with conditional adversarial networks,https://github.com/ImagingLab/Colorizing-with-GANs,Y
image-to-image translation with conditional adversarial networks,https://github.com/Adi-iitd/Image-Compositing,Y
image-to-image translation with conditional adversarial networks,https://github.com/NeuralVFX/pix2pix,N
image-to-image translation with conditional adversarial networks,https://github.com/dk67604/ImageToImageTranslation,Y
image-to-image translation with conditional adversarial networks,https://github.com/brade31919/SRGAN-tensorflow,N
image-to-image translation with conditional adversarial networks,https://github.com/Adi-iitd/AI-Art,Y
image-to-image translation with conditional adversarial networks,https://github.com/SoohyeonLee/Study_GAN,Y
image-to-image translation with conditional adversarial networks,https://github.com/telecombcn-dl/2018-dlcv-team2,N
image-to-image translation with conditional adversarial networks,https://github.com/awesomephant/vf-installation,N
image-to-image translation with conditional adversarial networks,https://github.com/hanwen0529/GAN-pix2pix-Keras,Y
image-to-image translation with conditional adversarial networks,https://github.com/darth-c0d3r/pix2pix,Y
image-to-image translation with conditional adversarial networks,https://github.com/znxlwm/pytorch-pix2pix,Y
image-to-image translation with conditional adversarial networks,https://github.com/saviaga/faceswap,Y
image-to-image translation with conditional adversarial networks,https://github.com/BrookInternSOMA/pix2pix-barcode,Y
image-to-image translation with conditional adversarial networks,https://github.com/cianfrocco-lab/GAN-for-Cryo-EM-image-denoising,Y
image-to-image translation with conditional adversarial networks,https://github.com/JobQiu/hackrice,Y
image-to-image translation with conditional adversarial networks,https://github.com/affinelayer/Pix2Pix-tensorflow,N
image-to-image translation with conditional adversarial networks,https://github.com/SeonbeomKim/TensorFlow-pix2pix,Y
image-to-image translation with conditional adversarial networks,https://github.com/utunga/pix2pix-tensorflow,Y
image-to-image translation with conditional adversarial networks,https://github.com/linxi159/Tips-and-tricks-to-train-GANs,N
image-to-image translation with conditional adversarial networks,https://github.com/phillipi/pix2pix,Y
image-to-image translation with conditional adversarial networks,https://github.com/acecreamu/Color-Constancy-pix2pix,N
image-to-image translation with conditional adversarial networks,https://github.com/kingcheng2000/GAN,N
image-to-image translation with conditional adversarial networks,https://github.com/misogil0116/Biscotti_misc,N
image-to-image translation with conditional adversarial networks,https://github.com/sdnr1/c-gan_pix2pix,Y
image-to-image translation with conditional adversarial networks,https://github.com/jaingaurav3/GAN-Hacks,N
image-to-image translation with conditional adversarial networks,https://github.com/itsuki8914/pix2pix-automaticColorization,Y
image-to-image translation with conditional adversarial networks,https://github.com/mgrcar/pix2pix,Y
image-to-image translation with conditional adversarial networks,https://github.com/tensorflow/models/tree/master/research/gan,Y
image-to-image translation with conditional adversarial networks,https://github.com/amitaydr/Hackaton2018,Y
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/gunpowder1473/CycleGan_Tensorflow,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/Yougunhee/cycleGAN,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/MarvinLavechin/imagetranslation-tensorflow,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/Qi-Xian/color-transfer_HW1,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/mingbocui/Cycle_GAN-implemented-by-fastai,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/lyonling/pix2pix,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/beginner1997/Newc,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/jason9693/cycleGAN-tensorflow-v2,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/racinmat/anime-style-transfer,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/TJJTJJTJJ/pytorch_cycleGAN,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/LynnHo/CycleGAN-Tensorflow-PyTorch-Simple,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/fuenwang/CVFX-hw1,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/tensorpack/tensorpack,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/tbullmann/imagetranslation-tensorflow,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/TJJTJJTJJ/pytorch_CycleGAN_frame,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/YouYueHuang/CycleGAN_Unsupervised_Domain_Adaptation,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/justusschock/delira_cycle_gan_pytorch,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/sweet-rytsar/cvfve_hw1,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/WeiYangze/hibernate-demo,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/JL321/CycleGAN,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/daintlab/ct-denoising,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/jpeeg/OOP-Cyclegan,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/xhujoy/CycleGAN-tensorflow,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/gunpowder1473/CycyleGan_Tensorflow,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/carnotaur/CycleGAN,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/GAN-Challenger/pytorch-CycleGAN-and-pix2pix,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/itsuki8914/CycleGAN-TF,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/QLightman/CDGAN,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/manicman1999/CycleGAN256-BR,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/jiajunhua/CycleGAN,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/mehdidc/any2any,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/ranery/Bayesian-CycleGAN,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/Eyyub/tensorflow-cyclegan,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/NathanDeMaria/AugmentedCycleGAN,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/QLightman/VRAR-Course-Project,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/NeuralVFX/lighting-swap-cyclegan,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/AlphaKong/CycleGAN,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/Adi-iitd/AI-Art,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/liuzeyuMr/CycleGAN-TensorFlow-master,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/onedayatatime0923/Cycle_Mcd_Gan,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/eugene08976/hw1,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/mori97/CycleGAN-chainer,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/junyanz/CycleGAN,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/bareeka/ganproject,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/karl-hajjar/Generative-Adversarial-Networks,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/SimeonKraev/Musical-style-transfer-with-cycle-consistent-generative-adversarial-neural-networks,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/Yonv1943/CloudRemoval,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/whitenightwu/CycleGAN-TensorFlow,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/tollymune/CycleGAN-PyTorch,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/aitorzip/PyTorch-CycleGAN,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/CoryRJ/cycleGAN,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/Shumway82/CycleGAN,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/khush3/GAN,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/vanhuyz/CycleGAN-TensorFlow,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/mronta/CycleGAN-in-Keras,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/RanBezen/cycleGan,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/RRoundTable/OceanLitter_dataset_generator,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/tensorflow/models/tree/master/research/gan,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/mickan123/CycleGAN,N
unpaired image-to-image translation using cycle-consistent adversarial networks,https://github.com/cvfx-2019/homework1-color-transfer,N
coupled generative adversarial networks,https://github.com/mingyuliutw/CoGAN,Y
learning from simulated and unsupervised images through adversarial training,https://github.com/tensorflow/models/tree/master/research/gan,N
learning from simulated and unsupervised images through adversarial training,https://github.com/rickyhan/SimGAN-Captcha,N
learning from simulated and unsupervised images through adversarial training,https://github.com/andrearama/Deep-Auxiliary-Classifier-GAN,N
learning from simulated and unsupervised images through adversarial training,https://github.com/jiajunhua/CycleGAN,N
adversarially learned inference,https://github.com/IshmaelBelghazi/ALI,N
adversarially learned inference,https://github.com/zhenxuan00/graphical-gan,Y
adversarially learned inference,https://github.com/pavasgdb/Anomaly-detector-using-GAN,Y
adversarially learned inference,https://github.com/9310gaurav/ali-pytorch,Y
adversarially learned inference,https://github.com/lkhphuc/Anomaly-XRay-GANs,Y
adversarially learned inference,https://github.com/lkhphuc/Anomaly-BiGAN,Y
semantic image synthesis with spatially-adaptive normalization,https://github.com/noyoshi/smart-sketch,Y
semantic image synthesis with spatially-adaptive normalization,https://github.com/manicman1999/StyleGAN-Keras,Y
semantic image synthesis with spatially-adaptive normalization,https://github.com/divyanshj16/SPADE,Y
semantic image synthesis with spatially-adaptive normalization,https://github.com/tinawu-23/smart-sketch,Y
semantic image synthesis with spatially-adaptive normalization,https://github.com/KushajveerSingh/SPADE-PyTorch,Y
semantic image synthesis with spatially-adaptive normalization,https://github.com/NVlabs/SPADE,Y
semantic image synthesis with spatially-adaptive normalization,https://github.com/noyoshi/hacksc,Y
high-resolution image synthesis and semantic manipulation with conditional gans,https://github.com/eyeswideopen/pix2pixHD-MED,N
high-resolution image synthesis and semantic manipulation with conditional gans,https://github.com/NVIDIA/pix2pixHD,Y
high-resolution image synthesis and semantic manipulation with conditional gans,https://github.com/LiuNull/pix2pix_Liu,N
high-resolution image synthesis and semantic manipulation with conditional gans,https://github.com/rickyHong/pix2pixHD-repl,N
high-resolution image synthesis and semantic manipulation with conditional gans,https://github.com/haru-256/pix2pixHD.pytorch,N
high-resolution image synthesis and semantic manipulation with conditional gans,https://github.com/edricwu/Testing-1,N
high-resolution image synthesis and semantic manipulation with conditional gans,https://github.com/wentao99/pix2pixHD,N
high-resolution image synthesis and semantic manipulation with conditional gans,https://github.com/mingyuliutw/UNIT,N
a curriculum domain adaptation approach to the semantic segmentation of urban scenes,https://github.com/YangZhang4065/AdaptationSeg,N
fcns in the wild: pixel-level adversarial and constraint-based adaptation,https://github.com/stu92054/Domain-adaptation-on-segmentation,N
fcns in the wild: pixel-level adversarial and constraint-based adaptation,https://github.com/Wanger-SJTU/FCN-in-the-wild,N
cycada: cycle-consistent adversarial domain adaptation,https://github.com/jhoffman/cycada_release,Y
stargan: unified generative adversarial networks for multi-domain image-to-image translation,https://github.com/AdibaOrz/stargan_try,N
stargan: unified generative adversarial networks for multi-domain image-to-image translation,https://github.com/SummerHuiZhang/StarGAN_test,N
stargan: unified generative adversarial networks for multi-domain image-to-image translation,https://github.com/Rongpeng-Lin/StarGAN-Multi-domain-use-tensorflow,N
stargan: unified generative adversarial networks for multi-domain image-to-image translation,https://github.com/yunjey/StarGAN,Y
stargan: unified generative adversarial networks for multi-domain image-to-image translation,https://github.com/cosmic119/StarGAN,N
stargan: unified generative adversarial networks for multi-domain image-to-image translation,https://github.com/itsuki8914/starGAN-LSGAN,N
stargan: unified generative adversarial networks for multi-domain image-to-image translation,https://github.com/SummerHuiZhang/StarGAN_Norland,N
stargan: unified generative adversarial networks for multi-domain image-to-image translation,https://github.com/sitharakannan/inf,N
invertible conditional gans for image editing,https://github.com/Guim3/IcGAN,Y
invertible conditional gans for image editing,https://github.com/LynnHo/AttGAN-Tensorflow,Y
invertible conditional gans for image editing,https://github.com/tangji08/face-generator,Y
dualgan: unsupervised dual learning for image-to-image translation,https://github.com/duxingren14/DualGAN,Y
large scale gan training for high fidelity natural image synthesis,https://github.com/ivclab/BigGAN-Generator-Pretrained-Pytorch,N
large scale gan training for high fidelity natural image synthesis,https://github.com/ANIME305/Anime-GAN-tensorflow,N
large scale gan training for high fidelity natural image synthesis,https://github.com/ANIME305/Anime-GAN,N
high-fidelity image generation with fewer labels,https://github.com/google/compare_gan,N
self-attention generative adversarial networks,https://github.com/brain-research/self-attention-gan,N
self-attention generative adversarial networks,https://github.com/yanqi1811/self-attention,Y
self-attention generative adversarial networks,https://github.com/hinofafa/Self-Attention-HearthStone-GAN,Y
self-attention generative adversarial networks,https://github.com/alfagao/DeOldify,Y
self-attention generative adversarial networks,https://github.com/llvictorll/AS,N
self-attention generative adversarial networks,https://github.com/Soldie/DeOldify-colorir-imagens-antigas,Y
self-attention generative adversarial networks,https://github.com/tnishida13/DeOldify24,Y
self-attention generative adversarial networks,https://github.com/ANIME305/Anime-GAN,N
self-attention generative adversarial networks,https://github.com/hinofafa/Self-Attention-GAN,Y
self-attention generative adversarial networks,https://github.com/kiyohiro8/SelfAttentionGAN,N
self-attention generative adversarial networks,https://github.com/jugatsingh/self_attention_video,Y
self-attention generative adversarial networks,https://github.com/heykeetae/Self-Attention-GAN,Y
self-attention generative adversarial networks,https://github.com/ankitAMD/Self-Attention-GAN-master_pytorch,Y
self-attention generative adversarial networks,https://github.com/akanimax/attn_gan_pytorch,N
self-attention generative adversarial networks,https://github.com/shaoanlu/faceswap-GAN,N
self-attention generative adversarial networks,https://github.com/jher123/GAN-experiments,N
self-attention generative adversarial networks,https://github.com/weishenho/SAGAN-with-relativistic,Y
self-attention generative adversarial networks,https://github.com/jher123/WGAN-experiments,N
self-attention generative adversarial networks,https://github.com/ANIME305/Anime-GAN-tensorflow,N
self-attention generative adversarial networks,https://github.com/ladadidadida/Self-Attention-GAN,Y
self-attention generative adversarial networks,https://github.com/ncuzzy/mygan,N
self-attention generative adversarial networks,https://github.com/rosinality/sagan-pytorch,N
self-attention generative adversarial networks,https://github.com/voletiv/self-attention-GAN-pytorch,N
self-attention generative adversarial networks,https://github.com/jantic/DeOldify,Y
cgans with projection discriminator,https://github.com/crcrpar/pytorch.sngan_projection,Y
cgans with projection discriminator,https://github.com/DanielLongo/cGANs,N
cgans with projection discriminator,https://github.com/DanielLongo/GANs,N
cgans with projection discriminator,https://github.com/pfnet-research/sngan_projection,N
cgans with projection discriminator,https://github.com/DanielLongo/AdversarialTrain,N
conditional image synthesis with auxiliary classifier gans,https://github.com/Mckinsey666/Anime-Generation,N
conditional image synthesis with auxiliary classifier gans,https://github.com/facebookresearch/pytorch_GAN_zoo,Y
conditional image synthesis with auxiliary classifier gans,https://github.com/VitoRazor/Gan_Architecture,Y
conditional image synthesis with auxiliary classifier gans,https://github.com/Hydrino/ACGAN_mnist,N
conditional image synthesis with auxiliary classifier gans,https://github.com/johnnyjana730/ACGAN_animate,N
conditional image synthesis with auxiliary classifier gans,https://github.com/andrearama/Deep-Auxiliary-Classifier-GAN,Y
conditional image synthesis with auxiliary classifier gans,https://github.com/Byte7/GANs,N
conditional image synthesis with auxiliary classifier gans,https://github.com/jeanjerome/semisupervised_timeseries_infogan,N
conditional image synthesis with auxiliary classifier gans,https://github.com/bunny98/Text-to-Image-Using-GAN,N
conditional image synthesis with auxiliary classifier gans,https://github.com/buriburisuri/ac-gan,N
conditional image synthesis with auxiliary classifier gans,https://github.com/ZhimingZhou/AM-GAN,N
conditional image synthesis with auxiliary classifier gans,https://github.com/asprenger/keras_acgan,N
conditional image synthesis with auxiliary classifier gans,https://github.com/Hydrino/ACGAN_cifar10,N
conditional image synthesis with auxiliary classifier gans,https://github.com/lukedeo/keras-acgan,N
conditional image synthesis with auxiliary classifier gans,https://github.com/Mckinsey666/ACGAN-Anime-Generation,N
conditional image synthesis with auxiliary classifier gans,https://github.com/jackyjsy/ACWGAN,Y
conditional image synthesis with auxiliary classifier gans,https://github.com/runoguler/Aux-GAN,N
conditional image synthesis with auxiliary classifier gans,https://github.com/Mckinsey666/ACGAN-Conditional-Anime-Generation,N
conditional image synthesis with auxiliary classifier gans,https://github.com/kaonashi-tyc/zi2zi,Y
improved training of wasserstein gans,https://github.com/mitscha/dplc,Y
improved training of wasserstein gans,https://github.com/jss367/thiskangaroodoesnotexist,Y
improved training of wasserstein gans,https://github.com/michael13162/DoodleGAN,N
improved training of wasserstein gans,https://github.com/spandan2/Wgan-GP_cats,Y
improved training of wasserstein gans,https://github.com/jackyjsy/ACWGAN,Y
improved training of wasserstein gans,https://github.com/igul222/improved_wgan_training,Y
improved training of wasserstein gans,https://github.com/dagrate/gan_network,Y
improved training of wasserstein gans,https://github.com/asierae/VoiceDCGAN,Y
improved training of wasserstein gans,https://github.com/thstkdgus35/EDSR-PyTorch,Y
improved training of wasserstein gans,https://github.com/seanmullery/iWGAN,Y
improved training of wasserstein gans,https://github.com/itsuki8914/wgan-gp-TensorFlow,Y
improved training of wasserstein gans,https://github.com/goldhuang/SRGAN-PyTorch,Y
improved training of wasserstein gans,https://github.com/lukovnikov/improved_wgan_training,Y
improved training of wasserstein gans,https://github.com/LynnHo/DCGAN-LSGAN-WGAN-WGAN-GP-Tensorflow,Y
improved training of wasserstein gans,https://github.com/chanshing/sobolev_gan,Y
improved training of wasserstein gans,https://github.com/t0nberryking/DCGAN256,Y
improved training of wasserstein gans,https://github.com/YuguangTong/improved_wgan_training,Y
improved training of wasserstein gans,https://github.com/WuChenshen/MeRGAN,Y
improved training of wasserstein gans,https://github.com/qnduan/wgan-scrna,Y
improved training of wasserstein gans,https://github.com/charlescheng0117/pytorch-WGAN-GP-TTUR-CelebA,Y
improved training of wasserstein gans,https://github.com/lilianweng/unified-gan-tensorflow,Y
improved training of wasserstein gans,https://github.com/AndreasWieg/PC-PGGAN,Y
improved training of wasserstein gans,https://github.com/donand/GAN_pytorch,Y
improved training of wasserstein gans,https://github.com/adler-j/bwgan,Y
improved training of wasserstein gans,https://github.com/yaxingwang/Transferring-GANs,Y
improved training of wasserstein gans,https://github.com/divyam25/Oh-My-GAN,N
improved training of wasserstein gans,https://github.com/markmaxt/VideoSR,Y
improved training of wasserstein gans,https://github.com/DevashishJoshi/Transferring-GANs-FYP,Y
improved training of wasserstein gans,https://github.com/jesse1029/Fake-Face-Images-Detection-Tensorflow,Y
improved training of wasserstein gans,https://github.com/houze-liu/vanilla-wasserstein-loss-sensitive-dcgan_gradient_penalty,Y
stacked generative adversarial networks,https://github.com/xunhuang1995/SGAN,Y
improved techniques for training gans,https://github.com/theidentity/Improved-GAN-PyTorch,Y
improved techniques for training gans,https://github.com/nanwei1/MNIST_GAN,Y
improved techniques for training gans,https://github.com/andrearama/Deep-Auxiliary-Classifier-GAN,Y
improved techniques for training gans,https://github.com/bruno-31/ImprovedGAN-Tensorflow,Y
improved techniques for training gans,https://github.com/healthcare-robotics/mr-gan,Y
improved techniques for training gans,https://github.com/agupta231/CARROL,Y
improved techniques for training gans,https://github.com/raahii/video-gans-evaluation,Y
improved techniques for training gans,https://github.com/ZhimingZhou/AM-GAN,N
improved techniques for training gans,https://github.com/zlenyk/NNCodesAndProjects,Y
improved techniques for training gans,https://github.com/zhenxuan00/triple-gan,Y
improved techniques for training gans,https://github.com/kseuro/gan_project,Y
improved techniques for training gans,https://github.com/sanghviyashiitb/GANS-VanillaAndMinibatchDiscrimination,Y
improved techniques for training gans,https://github.com/tdrussell/IllustrationGAN,Y
improved techniques for training gans,https://github.com/deepak112/Keras-SRGAN,Y
improved techniques for training gans,https://github.com/watsonyanghx/GAN_Lib_Tensorflow,Y
improved techniques for training gans,https://github.com/tensorflow/models/tree/master/research/gan,Y
improved techniques for training gans,https://github.com/t0nberryking/DCGAN256,Y
improved techniques for training gans,https://github.com/ctjoy/deep-learning-project,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/Mckinsey666/Anime-Generation,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/znxlwm/pytorch-MNIST-CelebA-GAN-DCGAN,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/XiafeiYu/DCGAN,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/MakeDirtyCode/DC-GAN-pytorch,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/jacobgil/keras-dcgan,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/Murali81/InfoGAN,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/eggplant60/dcgan_anime,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/darr/DCGAN,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/NicicDamjan/SoftKompjuting,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/Mckinsey666/ACGAN-Anime-Generation,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/llvictorll/AS,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/akanimax/dcgan_pytorch,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/anandchandra50/deep-space,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/pskrunner14/face-DCGAN,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/MustafaMustafa/cosmoGAN,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/junyanz/iGAN,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/Ayush-Goyal-coding/GANs,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/cshanjiewu/CVPR19,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/DanielLongo/GANs,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/taoxugit/AttnGAN,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/SimRunBot/DCGAN,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/prodillo/GAN_project,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/SoohyeonLee/Study_GAN,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/tensorlayer/dcgan,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/Natsu6767/DCGAN-PyTorch,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/Mckinsey666/ACGAN-Conditional-Anime-Generation,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/VIRUS-ATTACK/DCGAN-implementation.,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/Atharva-Phatak/GAN_MNIST,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/facebookresearch/pytorch_GAN_zoo,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/psmenon/DCGAN,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/suzana-ilic/DCGANs_pytorch,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/cachett/DCGANandCAE,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/HaijunMa/GAN-Getting-started-learning,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/vharaymonten/FaceGenerationWithGAN,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/cbxs123/Advanced-Deep-Learning-with-Keras,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/addy369/DCGANS,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/soyoung9306/GAN,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/captain-pool/DC-GAN,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/tomhata/emojigan,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/suzana-ilic/pytorch_DCGANs,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/vinayakkailas/DCGAN,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/soyoung9306/Advanced-Deep-Learning-with-Keras,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/MakeDirtyCode/cDCGAN-celebA-pytorch,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/kseuro/gan_project,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/mehdidc/hybrid-keras,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/mattya/chainer-DCGAN,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/JungminChung/dcgan_pytorch,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/donand/GAN_pytorch,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/DanielLongo/cGANs,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/iimuz/dcgan,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/HaijunMa/GAN,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/jesse1029/Fake-Face-Images-Detection-Tensorflow,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/Masquerade51256/DCGAN_SignPicture,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/tensorflow/models/tree/master/research/gan,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/jiajunhua/dcgan_code,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/HanxiansenEli/Machine-Learning,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/carpedm20/DCGAN-tensorflow,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/LynnHo/DCGAN-LSGAN-WGAN-WGAN-GP-Tensorflow,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/vohoaiviet/Advanced-Deep-Learning-with-Keras,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/yukia18/pytorch,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/gaurav6696/GAN,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/virafpatrawala/DCGAN,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/wlstyql/Pytorch,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/MustafaMustafa/dcgan-tf-benchmark,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/namepen/AI_school_proj,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/kpandey008/DCGANS,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/jpowie01/DCGAN_CelebA,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/Zotkin/ml_project,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/tdrussell/IllustrationGAN,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/DanielLongo/AdversarialTrain,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/bbclr20/TensorFlow-Examples,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/timsainb/Tensorflow-MultiGPU-VAE-GAN,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/horseriver/DCGAN--MatConvNet,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/MakeDirtyCode/DCGAN-celebA-pytorch,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/vmartinezalvarez/Image-generation-GAN-vs-DCGAN,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/Guim3/IcGAN,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/QuickSolverKyle/Tensorflow-MyGANs,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/akjgskwi/DCGAN_implement,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/TUM-ML-Lab18/FaceSwap,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/Ranchofromxgd/DCGAN_mnist,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/linxi159/dcgan_code,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/sugyan/tf-dcgan,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/qinpengzhi/myDCGAN,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/kuangshengkai/dcgan-keras,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/mchiappe83/Generate_Faces,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/hwyncho/GAN-Keras,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/SoumyadeepJana/CelebrityFaceGeneration,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/KostasTok/keras_DCGAN_transfer_learning,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/jparcill/gansbestfriend,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/zhangqianhui/DCGAN-tensorflow,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/AndreyBesedin/ImageClassification,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/pearsonkyle/Neural-Nebula,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/pearsonkyle/Artificial-Art,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/sahand68/Deep-Convolutional-GAN,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/ChengBinJin/WGAN-TensorFlow,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/sherdencooper/dcgan-mnist,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/richardrl/gan-pytorch,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/VIRUS-ATTACK/DCGAN-GPU-IMPLEMENTATION,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/Newmu/dcgan_code,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/johnnyjana730/keras_dcgan_car,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/esslushy/AbstractArtGenerator,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/mickaelChen/GMV,N
unsupervised representation learning with deep convolutional generative adversarial networks,https://github.com/divyam25/Oh-My-GAN,N
learning to draw samples: with application to amortized mle for generative adversarial learning,https://github.com/DartML/SteinGAN,N
coco-gan: generation by parts via conditional coordinating,https://github.com/hubert0527/COCO-GAN,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/andrewmaurer/GAN-Talk,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/TUM-ML-Lab18/FaceSwap,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/zsef123/PGGAN-Pytorch,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/akanimax/T2F,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/alfagao/DeOldify,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/stevehansen43/progressive_GAN,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/tkarras/progressive_growing_of_gans,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/Soldie/DeOldify-colorir-imagens-antigas,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/tnishida13/DeOldify24,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/thenhz/progressive_growing_of_gan,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/clintonjwang/pgan,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/random-quark/progressive-gan-completion,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/musikisomorphie/swd,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/civilman628/pgan,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/eric-erki/progressive_growing_of_gans,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/MNaplesDevelopment/MNIST-DCGAN-PyTorch,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/MNaplesDevelopment/DCGAN-PyTorch,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/podgorskiy/StyleGan,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/AndreasWieg/PC-PGGAN,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/facebookresearch/pytorch_GAN_zoo,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/jonasz/progressive_infogan,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/MNaplesDevelopment/Deep-Convolutional-Generative-Adversarial-Networks,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/akanimax/pro_gan_pytorch,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/timsainb/GAIA,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/axium/Blind-Image-Deconvolution-using-Deep-Generative-Priors,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/jantic/DeOldify,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/Latope2-150/Progressive_Growing_of_GANs-PyTorch,N
"progressive growing of gans for improved quality, stability, and variation",https://github.com/jeromerony/Progressive_Growing_of_GANs-PyTorch,N
stackgan++: realistic image synthesis with stacked generative adversarial networks,https://github.com/kkanshul/finegan,N
stackgan++: realistic image synthesis with stacked generative adversarial networks,https://github.com/hanzhanggit/StackGAN-Pytorch,N
stackgan++: realistic image synthesis with stacked generative adversarial networks,https://github.com/akanimax/T2F,N
stackgan++: realistic image synthesis with stacked generative adversarial networks,https://github.com/Maymaher/StackGANv2,Y
stackgan++: realistic image synthesis with stacked generative adversarial networks,https://github.com/taoxugit/AttnGAN,N
stackgan++: realistic image synthesis with stacked generative adversarial networks,https://github.com/hanzhanggit/StackGAN-v2,Y
stackgan++: realistic image synthesis with stacked generative adversarial networks,https://github.com/hanzhanggit/StackGAN,N
attngan: fine-grained text to image generation with attentional generative adversarial networks,https://github.com/roxanasoto/AttGanESRGAN,N
attngan: fine-grained text to image generation with attentional generative adversarial networks,https://github.com/Maymaher/StackGANv2,N
attngan: fine-grained text to image generation with attentional generative adversarial networks,https://github.com/aleksey-egorov/attngan,N
attngan: fine-grained text to image generation with attentional generative adversarial networks,https://github.com/ChihchengHsieh/AttnGAN_Implementation,N
attngan: fine-grained text to image generation with attentional generative adversarial networks,https://github.com/hanzhanggit/StackGAN-v2,N
attngan: fine-grained text to image generation with attentional generative adversarial networks,https://github.com/AnuragKatakkar/BE-Project,N
generating multiple objects at spatially distinct locations,https://github.com/tohinz/multiple-objects-gan,N
generating images from captions with attention,https://github.com/emansim/text2image,Y
learning symmetry consistent deep cnns for face completion,https://github.com/csxmli2016/SymmFCNet,N
a style-based generator architecture for generative adversarial networks,https://github.com/facebookresearch/pytorch_GAN_zoo,Y
a style-based generator architecture for generative adversarial networks,https://github.com/rosinality/style-based-gan-pytorch,Y
a style-based generator architecture for generative adversarial networks,https://github.com/perplexingpegasus/EarthLandscapeGAN,Y
a style-based generator architecture for generative adversarial networks,https://github.com/manicman1999/StyleGAN-Keras,Y
a style-based generator architecture for generative adversarial networks,https://github.com/pfnet-research/chainer-stylegan,Y
a style-based generator architecture for generative adversarial networks,https://github.com/NVlabs/ffhq-dataset,N
a style-based generator architecture for generative adversarial networks,https://github.com/danieldunderfelt/stylegan-experiments,Y
a style-based generator architecture for generative adversarial networks,https://github.com/RUTILEA/Chainer-StyleBasedGAN,Y
a style-based generator architecture for generative adversarial networks,https://github.com/NVlabs/stylegan,N
a style-based generator architecture for generative adversarial networks,https://github.com/podgorskiy/StyleGan,Y
improving mmd-gan training with repulsive loss function,https://github.com/richardwth/MMD-GAN,N
spectral normalization for generative adversarial networks,https://github.com/apnkv/nla_spectral_norm,Y
spectral normalization for generative adversarial networks,https://github.com/yanqi1811/self-attention,Y
spectral normalization for generative adversarial networks,https://github.com/hinofafa/Self-Attention-HearthStone-GAN,Y
spectral normalization for generative adversarial networks,https://github.com/julianalverio/pytorchrl,Y
spectral normalization for generative adversarial networks,https://github.com/hinofafa/Self-Attention-GAN,Y
spectral normalization for generative adversarial networks,https://github.com/Bingwen-Hu/DRIT,Y
spectral normalization for generative adversarial networks,https://github.com/yeonwoo90/convs,Y
spectral normalization for generative adversarial networks,https://github.com/meg965/pytorch-rl,Y
spectral normalization for generative adversarial networks,https://github.com/jugatsingh/self_attention_video,Y
spectral normalization for generative adversarial networks,https://github.com/heykeetae/Self-Attention-GAN,Y
spectral normalization for generative adversarial networks,https://github.com/rahulbhalley/turing-gan.pytorch,Y
spectral normalization for generative adversarial networks,https://github.com/Xiaoming-Yu/SingleGAN,Y
spectral normalization for generative adversarial networks,https://github.com/ankitAMD/Self-Attention-GAN-master_pytorch,Y
spectral normalization for generative adversarial networks,https://github.com/guy-oren/DIRT-OST,Y
spectral normalization for generative adversarial networks,https://github.com/VitoRazor/Gan_Architecture,Y
spectral normalization for generative adversarial networks,https://github.com/crcrpar/pytorch.sngan_projection,Y
spectral normalization for generative adversarial networks,https://github.com/ladadidadida/Self-Attention-GAN,Y
spectral normalization for generative adversarial networks,https://github.com/ncuzzy/mygan,N
spectral normalization for generative adversarial networks,https://github.com/ksagit/sn_gan_pytorch,Y
spectral normalization for generative adversarial networks,https://github.com/navneet-nmk/pytorch-rl,Y
spectral normalization for generative adversarial networks,https://github.com/pfnet-research/sngan_projection,N
msg-gan: multi-scale gradient gan for stable image synthesis,https://github.com/akanimax/BMSG-GAN,Y
on gradient regularizers for mmd gans,https://github.com/MichaelArbel/Scaled-MMD-GAN,Y
lr-gan: layered recursive generative adversarial networks for image generation,https://github.com/jwyang/lr-gan.pytorch,N
calibrating energy-based generative adversarial networks,https://github.com/zihangdai/cegan_iclr2017,N
generative multi-adversarial networks,https://github.com/iDurugkar/GMAN,N
began: boundary equilibrium generative adversarial networks,https://github.com/tensorpack/tensorpack,N
began: boundary equilibrium generative adversarial networks,https://github.com/noko-git/began,Y
began: boundary equilibrium generative adversarial networks,https://github.com/carpedm20/BEGAN-tensorflow,Y
began: boundary equilibrium generative adversarial networks,https://github.com/taey16/pix2pixBEGAN.pytorch,Y
began: boundary equilibrium generative adversarial networks,https://github.com/eliceio/vocal-style-transfer,Y
began: boundary equilibrium generative adversarial networks,https://github.com/JimmyDoan1309/BEGAN---Tensorflow-implementation,Y
began: boundary equilibrium generative adversarial networks,https://github.com/larsjaakko/cryptokitty-gan,Y
began: boundary equilibrium generative adversarial networks,https://github.com/Heumi/BEGAN-tensorflow,Y
began: boundary equilibrium generative adversarial networks,https://github.com/timsainb/GAIA,Y
began: boundary equilibrium generative adversarial networks,https://github.com/davidismael/BEGAN,Y
began: boundary equilibrium generative adversarial networks,https://github.com/artcg/BEGAN,Y
nice: non-linear independent components estimation,https://github.com/huicongzhang/FLOW,Y
nice: non-linear independent components estimation,https://github.com/HUJI-Deep/nice,Y
nice: non-linear independent components estimation,https://github.com/ermongroup/a-nice-mc,Y
nice: non-linear independent components estimation,https://github.com/paultsw/nice_pytorch,Y
nice: non-linear independent components estimation,https://github.com/laurent-dinh/nice,Y
draw: a recurrent neural network for image generation,https://github.com/suhoy901/DRAW_pytorch,Y
draw: a recurrent neural network for image generation,https://github.com/numberlearning/DRAM,Y
draw: a recurrent neural network for image generation,https://github.com/Natsu6767/Generating-Devanagari-Using-DRAW,Y
draw: a recurrent neural network for image generation,https://github.com/vivanov879/draw,Y
draw: a recurrent neural network for image generation,https://github.com/ericjang/draw,Y
draw: a recurrent neural network for image generation,https://github.com/jlindsey15/DRAM,Y
draw: a recurrent neural network for image generation,https://github.com/jbornschein/draw,N
draw: a recurrent neural network for image generation,https://github.com/yashkalani/DRAW,N
draw: a recurrent neural network for image generation,https://github.com/singhsidhukuldeep/Generating-Devanagari-Using-DRAW,Y
draw: a recurrent neural network for image generation,https://github.com/nHeidelberg/Pytorch-Implementation-DRAW,Y
density estimation using real nvp,https://github.com/gtegner/real_nvp,Y
density estimation using real nvp,https://github.com/chrischute/real-nvp,Y
density estimation using real nvp,https://github.com/taesung89/real-nvp,Y
density estimation using real nvp,https://github.com/wjy5446/pytorch-Real-NVP,Y
density estimation using real nvp,https://github.com/tensorflow/models/tree/master/research/real_nvp,Y
density estimation using real nvp,https://github.com/ars-ashuha/real-nvp-pytorch,Y
density estimation using real nvp,https://github.com/kamenbliznashki/normalizing_flows,N
density estimation using real nvp,https://github.com/li012589/NeuralRG,Y
improved variational inference with inverse autoregressive flow,https://github.com/openai/iaf,N
gans trained by a two time-scale update rule converge to a local nash equilibrium,https://github.com/tnishida13/DeOldify24,N
gans trained by a two time-scale update rule converge to a local nash equilibrium,https://github.com/agupta231/CARROL,N
gans trained by a two time-scale update rule converge to a local nash equilibrium,https://github.com/alfagao/DeOldify,N
gans trained by a two time-scale update rule converge to a local nash equilibrium,https://github.com/bioinf-jku/TTUR,N
gans trained by a two time-scale update rule converge to a local nash equilibrium,https://github.com/mseitzer/pytorch-fid,N
gans trained by a two time-scale update rule converge to a local nash equilibrium,https://github.com/charlescheng0117/pytorch-WGAN-GP-TTUR-CelebA,N
gans trained by a two time-scale update rule converge to a local nash equilibrium,https://github.com/watsonyanghx/GAN_Lib_Tensorflow,N
gans trained by a two time-scale update rule converge to a local nash equilibrium,https://github.com/DevashishJoshi/Transferring-GANs-FYP,N
gans trained by a two time-scale update rule converge to a local nash equilibrium,https://github.com/yenchenlin/fid,N
gans trained by a two time-scale update rule converge to a local nash equilibrium,https://github.com/minhnhat93/tf-SNDCGAN,N
gans trained by a two time-scale update rule converge to a local nash equilibrium,https://github.com/jantic/DeOldify,N
gans trained by a two time-scale update rule converge to a local nash equilibrium,https://github.com/raahii/video-gans-evaluation,N
gans trained by a two time-scale update rule converge to a local nash equilibrium,https://github.com/hidekifujinami6/gan_evaluation,N
gans trained by a two time-scale update rule converge to a local nash equilibrium,https://github.com/djl11/mxnet-fid,N
gans trained by a two time-scale update rule converge to a local nash equilibrium,https://github.com/AlexiaJM/Deep-learning-with-cats,N
gans trained by a two time-scale update rule converge to a local nash equilibrium,https://github.com/Soldie/DeOldify-colorir-imagens-antigas,N
the relativistic discriminator: a key element missing from standard gan,https://github.com/jpjuvo/64-3D-RaSGAN,Y
the relativistic discriminator: a key element missing from standard gan,https://github.com/weishenho/SAGAN-with-relativistic,Y
the relativistic discriminator: a key element missing from standard gan,https://github.com/AlexiaJM/GANsBeyondDivergenceMin,Y
the relativistic discriminator: a key element missing from standard gan,https://github.com/AlexiaJM/RelativisticGAN,Y
first order generative adversarial networks,https://github.com/zalandoresearch/first_order_gan,Y
mode seeking generative adversarial networks for diverse image synthesis,https://github.com/HelenMao/MSGAN,Y
pixelcnn++: improving the pixelcnn with discretized logistic mixture likelihood and other modifications,https://github.com/pclucas14/pixel-cnn-pp,N
pixelcnn++: improving the pixelcnn with discretized logistic mixture likelihood and other modifications,https://github.com/openai/pixel-cnn,N
pixel recurrent neural networks,https://github.com/vocong25/gated_pixelcnn,Y
pixel recurrent neural networks,https://github.com/tccnchsu/Artifical_Intelegent,Y
pixel recurrent neural networks,https://github.com/arcelien/hawc-deep-learning,Y
pixel recurrent neural networks,https://github.com/singh-hrituraj/PixelCNN-Pytorch,Y
pixel recurrent neural networks,https://github.com/ardapekis/pixel-rnn,Y
conditional image generation with pixelcnn decoders,https://github.com/vocong25/gated_pixelcnn,Y
conditional image generation with pixelcnn decoders,https://github.com/SJHNJU/Gated_PixelCNN,Y
conditional image generation with pixelcnn decoders,https://github.com/openai/pixel-cnn,Y
conditional image generation with pixelcnn decoders,https://github.com/chandlersupple/Tensorflow-PixelCNN,Y
conditional image generation with pixelcnn decoders,https://github.com/SJHNJU/gated-pixelcnn,Y
glow: generative flow with invertible 1x1 convolutions,https://github.com/musyoku/chainer-glow,Y
glow: generative flow with invertible 1x1 convolutions,https://github.com/rosinality/glow-pytorch,Y
glow: generative flow with invertible 1x1 convolutions,https://github.com/openai/glow,N
glow: generative flow with invertible 1x1 convolutions,https://github.com/musyoku/generative-flow,Y
glow: generative flow with invertible 1x1 convolutions,https://github.com/chrischute/glow,Y
glow: generative flow with invertible 1x1 convolutions,https://github.com/ex4sperans/variational-inference-with-normalizing-flows,Y
glow: generative flow with invertible 1x1 convolutions,https://github.com/kamenbliznashki/normalizing_flows,N
towards conceptual compression,https://github.com/musyoku/convolutional-draw,Y
sliced wasserstein generative models,https://github.com/musikisomorphie/swd,Y
rmdl: random multimodel deep learning for classification,https://github.com/kk7nc/RMDL,Y
s$^3$fd: single shot scale-invariant face detector,https://github.com/sfzhang15/SFD,Y
s$^3$fd: single shot scale-invariant face detector,https://github.com/LeeRel1991/SFD,Y
robust face detection via learning small faces on hard images,https://github.com/bairdzhang/smallhardface,N
"hyperface: a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition",https://github.com/pasrichashivam/hyperface_deep_multi-task-learning_keras_implementation,N
"hyperface: a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition",https://github.com/takiyu/hyperface,N
recurrent scale approximation for object detection in cnn,https://github.com/sciencefans/RSA-for-object-detection,Y
faceboxes: a cpu real-time face detector with high accuracy,https://github.com/sfzhang15/FaceBoxes,Y
faceboxes: a cpu real-time face detector with high accuracy,https://github.com/TropComplique/FaceBoxes-tensorflow,Y
faceboxes: a cpu real-time face detector with high accuracy,https://github.com/XiaXuehai/faceboxes,N
faceboxes: a cpu real-time face detector with high accuracy,https://github.com/zisianw/FaceBoxes.PyTorch,Y
face detection with end-to-end integration of a convnet and a 3d model,https://github.com/tfwu/FaceDetection-ConvNet-3D,Y
dsfd: dual shot face detector,https://github.com/StarStyleSky/awesome-face-detection,N
dsfd: dual shot face detector,https://github.com/TencentYoutuResearch/FaceDetection-DSFD,Y
pyramidbox: a context-assisted single shot face detector,https://github.com/Swybino/PotatoNet,Y
pyramidbox: a context-assisted single shot face detector,https://github.com/PaddlePaddle/models,N
pyramidbox: a context-assisted single shot face detector,https://github.com/EricZgw/PyramidBox,Y
finding tiny faces,https://github.com/iitmcvg/Demo-of-face-detection-and-counting-,Y
finding tiny faces,https://github.com/chinakook/hr101_mxnet,Y
finding tiny faces,https://github.com/cydonia999/Tiny_Faces_in_Tensorflow,Y
finding tiny faces,https://github.com/Speacer/Super-Resolution-Generative-Adversarial-Networks,Y
finding tiny faces,https://github.com/shahidul56/Tiny_Faces_in_Tensorflow_msc,Y
finding tiny faces,https://github.com/tmdgus1237/HelloWorld2018-2,Y
finding tiny faces,https://github.com/peiyunh/tiny,Y
finding tiny faces,https://github.com/alexattia/ExtendedTinyFaces,Y
finding tiny faces,https://github.com/varununleashed/tiny_faces_temp,Y
joint face detection and alignment using multi-task cascaded convolutional networks,https://github.com/jimmy0087/faceai-master,N
joint face detection and alignment using multi-task cascaded convolutional networks,https://github.com/TropComplique/mtcnn-pytorch,N
joint face detection and alignment using multi-task cascaded convolutional networks,https://github.com/wanjinchang/MTCNN_USE_TF_E2E,N
joint face detection and alignment using multi-task cascaded convolutional networks,https://github.com/mdsarfarazulh/face-verification-facenet-mtcnn,N
joint face detection and alignment using multi-task cascaded convolutional networks,https://github.com/SSusantAchary/FaceDetection-counting-using-MTCNN,N
joint face detection and alignment using multi-task cascaded convolutional networks,https://github.com/iitmcvg/Demo-of-face-detection-and-counting-,N
joint face detection and alignment using multi-task cascaded convolutional networks,https://github.com/cydonia999/VGGFace2-pytorch,N
joint face detection and alignment using multi-task cascaded convolutional networks,https://github.com/alexattia/ExtendedTinyFaces,N
joint face detection and alignment using multi-task cascaded convolutional networks,https://github.com/pengsongyou/OMG-ADSC,N
wider face: a face detection benchmark,https://github.com/kabrau/FaceDetection,Y
a unified multi-scale deep convolutional neural network for fast object detection,https://github.com/zhaoweicai/mscnn,N
seqface: make full use of sequence information for face recognition,https://github.com/huangyangyu/SeqFace,Y
arcface: additive angular margin loss for deep face recognition,https://github.com/foamliu/InsightFace-v2,Y
arcface: additive angular margin loss for deep face recognition,https://github.com/chenggongliang/arcface,Y
arcface: additive angular margin loss for deep face recognition,https://github.com/Soldie/insightface-Rec.Face,Y
arcface: additive angular margin loss for deep face recognition,https://github.com/eric-erki/insightface,Y
arcface: additive angular margin loss for deep face recognition,https://github.com/larsoncs/face_detect,Y
arcface: additive angular margin loss for deep face recognition,https://github.com/longchr123/insightface-analysis,Y
arcface: additive angular margin loss for deep face recognition,https://github.com/zrui94/insight_mx,Y
arcface: additive angular margin loss for deep face recognition,https://github.com/seriousmac/face-recognition,Y
arcface: additive angular margin loss for deep face recognition,https://github.com/happynear/AMSoftmax,Y
arcface: additive angular margin loss for deep face recognition,https://github.com/foamliu/InsightFace,N
arcface: additive angular margin loss for deep face recognition,https://github.com/1996scarlet/ArcFace-Multiplex-Recognition,Y
arcface: additive angular margin loss for deep face recognition,https://github.com/deepinsight/insightface,Y
arcface: additive angular margin loss for deep face recognition,https://github.com/394781865/insightface,Y
arcface: additive angular margin loss for deep face recognition,https://github.com/modelhub-ai/arc-face,Y
arcface: additive angular margin loss for deep face recognition,https://github.com/gregoiredervaux/face_recognition,N
arcface: additive angular margin loss for deep face recognition,https://github.com/thongdk8/realtime-facereccpp,Y
arcface: additive angular margin loss for deep face recognition,https://github.com/sunil-rival/insightface,Y
quality aware network for set to set recognition,https://github.com/sciencefans/Quality-Aware-Network,Y
a light cnn for deep face representation with noisy labels,https://github.com/AlfredXiangWu/LightCNN,Y
a light cnn for deep face representation with noisy labels,https://github.com/AlfredXiangWu/face_verification_experiment,Y
git loss for deep face recognition,https://github.com/kjanjua26/Git-Loss-For-Deep-Face-Recognition,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/akshaybahadur21/Facial-Recognition-using-Facenet,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/chenyeheng/SmartCar-FaceRecognition,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/M155K4R4/facenet,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/IsharaSandun/Face-Recognition-using-python,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/CharlesPikachu/CharlesFace,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/roysai/Humpback-whale-identity-challenge,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/seriousmac/face-recognition,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/AmrTarekk/Attendence-system-using-Facial-Recognition-,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/aditya26-09/Face_Recognition,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/0b3d/loc2map,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/vinayakkailas/Face_Recognition,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/alexattia/ExtendedTinyFaces,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/developbyhunny/I-FINDER,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/mohitwildbeast/Facial-Recognition-Using-FaceNet-Siamese-One-Shot-Learning,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/rohansuresh27/FaceNet_face_verification,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/Anil1331/Facenet,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/coronnie/HouseGuard,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/HanxiansenEli/Machine-Learning,N
facenet: a unified embedding for face recognition and clustering,https://github.com/midhun-em/face_recognition_facenet,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/BradNeuberg/personal-photos-model,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/aobaruwa/TensorFlow-Projects,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/ArturPrzybysz/siamese-CNN-cat-vs-dogs,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/mdsarfarazulh/face-verification-facenet-mtcnn,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/KrenAnastasiya/face_recognition,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/hakaneroztekin/face-recognition,N
facenet: a unified embedding for face recognition and clustering,https://github.com/StPauls-Computer-Science/ng-lab-face-recognition,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/jooyounghun/AI-Team-5,N
facenet: a unified embedding for face recognition and clustering,https://github.com/davidsandberg/facenet,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/giovanniguidi/face_recognition-tf,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/Maninder10/face,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/andreidore/image_similarity,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/iitmcvg/Demo-of-face-detection-and-counting-,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/IcewineChen/mxnet-batch_hard_triplet_loss,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/phongdinhv/triplet-loss-keras-mnist,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/yehengchen/SmartCar-FaceRecognition,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/MohammadBakir/Face-Recognition-Happy-House,N
facenet: a unified embedding for face recognition and clustering,https://github.com/tbmoon/facenet,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/chenyeheng/SmartCar-FaceRec,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/YxZhxn/Instance-Search-Based-on-Triplet-Training-for-Feature-Representation,N
facenet: a unified embedding for face recognition and clustering,https://github.com/jadevaibhav/Signature-verification-using-deep-learning,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/itanish/Face-Recognition,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/madhavambati/Face-Recognition,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/MohamadMerchant/Voice-Authentication-and-Face-Recognition,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/ffr4nz/UnknownUnknowns,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/llSeedll/Facial-Recognition-using-Facenet,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/susmith98/Face_Recognition,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/sainimohit23/FaceNet-Real-Time-face-recognition,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/chroniccrash/facestuff,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/deepakks1995/inception,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/kaichengyan/facenet-darknet-pytorch,Y
facenet: a unified embedding for face recognition and clustering,https://github.com/chenyeheng/SmartCar,Y
sphereface: deep hypersphere embedding for face recognition,https://github.com/vohoaiviet/sphereface,Y
sphereface: deep hypersphere embedding for face recognition,https://github.com/wy1iu/sphereface,Y
sphereface: deep hypersphere embedding for face recognition,https://github.com/sevenHsu/Face_Recognition_IN_Video,Y
sphereface: deep hypersphere embedding for face recognition,https://github.com/zuoqing1988/mobile-sphereface-caffe,Y
regressing robust and discriminative 3d morphable models with a very deep neural network,https://github.com/anhttran/3dmm_cnn,Y
regressing robust and discriminative 3d morphable models with a very deep neural network,https://github.com/fengju514/Expression-Net,N
pose-robust face recognition via deep residual equivariant mapping,https://github.com/penincillin/DREAM,Y
faceposenet: making a case for landmark-free face alignment,https://github.com/fengju514/Face-Pose-Net,Y
faceposenet: making a case for landmark-free face alignment,https://github.com/fengju514/Expression-Net,Y
triplet probabilistic embedding for face verification and clustering,https://github.com/Ananaskelly/TPE,Y
look across elapse: disentangled representation learning and photorealistic cross-age face synthesis for age-invariant face recognition,https://github.com/ZhaoJ9014/High_Performance_Face_Recognition,N
ring loss: convex feature normalization for face recognition,https://github.com/vsatyakumar/Ring-Loss-Keras,Y
ring loss: convex feature normalization for face recognition,https://github.com/Paralysis/ringloss,Y
nonlinear 3d face morphable model,https://github.com/tranluan/Nonlinear_Face_3DMM,N
deep multi-center learning for face alignment,https://github.com/ZhiwenShao/MCNet-Extension,N
deep alignment network: a convolutional neural network for robust face alignment,https://github.com/MarekKowalski/DeepAlignmentNetwork,N
deep alignment network: a convolutional neural network for robust face alignment,https://github.com/justusschock/deep_alignment_network_pytorch,N
look at boundary: a boundary-aware face alignment algorithm,https://github.com/zhusz/CVPR15-CFSS,Y
joint 3d face reconstruction and dense face alignment from a single image with 2d-assisted self-supervised learning,https://github.com/XgTu/2DASL,N
joint 3d face reconstruction and dense face alignment from a single image with 2d-assisted self-supervised learning,https://github.com/XgTu/2DASL-CNN,N
joint 3d face reconstruction and dense alignment with position map regression network,https://github.com/YadiraF/PRNet,Y
joint 3d face reconstruction and dense alignment with position map regression network,https://github.com/jimmy0087/faceai-master,N
covariance pooling for facial expression recognition,https://github.com/d-acharya/CovPoolFER,N
greedy search for descriptive spatial face features,https://github.com/bbenligiray/greedy-face-features,Y
dexpression: deep convolutional neural network for expression recognition,https://github.com/MaxLikesMath/DeepLearningImplementations,N
ganfit: generative adversarial network fitting for high fidelity 3d face reconstruction,https://github.com/barisgecer/ganfit,N
unsupervised training for 3d morphable model regression,https://github.com/google/tf_mesh_renderer,Y
style aggregated network for facial landmark detection,https://github.com/D-X-Y/SAN,Y
deep label distribution learning with label ambiguity,https://github.com/gaobb/DLDL,N
facial aging and rejuvenation by conditional multi-adversarial autoencoder with ordinal regression,https://github.com/kingsj0405/CMAAE-OR,N
consistent rank logits for ordinal regression with convolutional neural networks,https://github.com/Raschka-research-group/coral-cnn,N
consistent rank logits for ordinal regression with convolutional neural networks,https://github.com/mshehrozsajjad/Age-Classification,N
learning generalizable and identity-discriminative representations for face anti-spoofing,https://github.com/XgTu/GFA-CNN,N
semi-supervised learning for face sketch synthesis in the wild,https://github.com/chaofengc/Face-Sketch-Wild,N
high-quality facial photo-sketch synthesis using multi-adversarial networks,https://github.com/lidan1/PhotoSketchMAN,N
multi-view dynamic facial action unit detection,https://github.com/BCV-Uniandes/AUNets,N
transferring rich deep features for facial beauty prediction,https://github.com/lucasxlu/TransFBP,N
scut-fbp5500: a diverse benchmark dataset for multi-paradigm facial beauty prediction,https://github.com/HCIILAB/SCUT-FBP5500-Database-Release,Y
high-level semantic feature detection:a new perspective for pedestrian detection,https://github.com/liuwei16/CSP,N
repulsion loss: detecting pedestrians in a crowd,https://github.com/bailvwangzi/repulsion_loss_ssd,Y
learning efficient single-stage pedestrian detectors by asymptotic localization fitting,https://github.com/VideoObjectSearch/ALFNet,N
illuminating pedestrians via simultaneous detection & segmentation,https://github.com/Ricardozzf/sdsrcnn,N
part-level convolutional neural networks for pedestrian detection using saliency and boundary box alignment,https://github.com/iyyun/Part-CNN,N
vpgnet: vanishing point guided network for lane and road marking detection and recognition,https://github.com/SeokjuLee/VPGNet,N
spatial as deep: spatial cnn for traffic scene understanding,https://github.com/GuangyanZhang/Paddle-Paddle_SCNN-Deeplabv3-bisenet-icnet,Y
spatial as deep: spatial cnn for traffic scene understanding,https://github.com/XingangPan/SCNN,Y
spatial as deep: spatial cnn for traffic scene understanding,https://github.com/GuangyanZhang/SCNN-Deeplabv3-bisenet-icnet,Y
learning to cluster for proposal-free instance segmentation,https://github.com/GT-RIPL/L2C,N
towards end-to-end lane detection: an instance segmentation approach,https://github.com/harryhan618/LaneNet,Y
towards end-to-end lane detection: an instance segmentation approach,https://github.com/cv-team/lane_detection_ML,N
towards end-to-end lane detection: an instance segmentation approach,https://github.com/klintan/lanenet-pytorch,Y
towards end-to-end lane detection: an instance segmentation approach,https://github.com/MaybeShewill-CV/lanenet-lane-detection,N
towards end-to-end lane detection: an instance segmentation approach,https://github.com/ShenhanQian/Lane-detection_an-instance-segmentation-method,N
semantic instance segmentation with a discriminative loss function,https://github.com/harryhan618/LaneNet,Y
semantic instance segmentation with a discriminative loss function,https://github.com/alicranck/instance-seg,N
a hierarchical deep architecture and mini-batch selection method for joint traffic sign and light detection,https://github.com/bosch-ros-pkg/bstld,N
multi-column deep neural networks for image classification,https://github.com/hughperkins/DeepCL,Y
micronnet: a highly compact deep convolutional neural network architecture for real-time embedded traffic sign classification,https://github.com/ppriyank/MicronNet,N
hydraplus-net: attentive deep features for pedestrian analysis,https://github.com/xh-liu/HydraPlus-Net,N
unsupervised cross-domain image generation,https://github.com/kaonashi-tyc/zi2zi,Y
adversarial discriminative domain adaptation,https://github.com/vingving/pytorch-domain-adaptation,N
adversarial discriminative domain adaptation,https://github.com/Carl0520/ADDA-pytorch,Y
adversarial discriminative domain adaptation,https://github.com/Fujiki-Nakamura/ADDA.PyTorch,Y
unsupervised domain adaptation by backpropagation,https://github.com/tachitachi/GradientReversal,Y
unsupervised domain adaptation by backpropagation,https://github.com/Carl0520/DANN_pytorch-,Y
unsupervised domain adaptation by backpropagation,https://github.com/erlendd/ddan,Y
unsupervised domain adaptation by backpropagation,https://github.com/bwxu/FNC-SNLI-FEVER-Domain-Adaptation,Y
unsupervised domain adaptation by backpropagation,https://github.com/vingving/pytorch-domain-adaptation,N
diverse image-to-image translation via disentangled representations,https://github.com/Bingwen-Hu/DRIT,Y
diverse image-to-image translation via disentangled representations,https://github.com/HsinYingLee/DRIT,N
diverse image-to-image translation via disentangled representations,https://github.com/guy-oren/DIRT-OST,Y
diverse image-to-image translation via disentangled representations,https://github.com/hytseng0509/DRIT_hr,N
diverse image-to-image translation via disentangled representations,https://github.com/HsinYingLee/MDMM,N
learning to adapt structured output space for semantic segmentation,https://github.com/zqwhu/SegDAwithBoundary,N
learning to adapt structured output space for semantic segmentation,https://github.com/stu92054/Domain-adaptation-on-segmentation,Y
learning to adapt structured output space for semantic segmentation,https://github.com/jizongFox/ReproduceAdaptSegNet,N
learning to adapt structured output space for semantic segmentation,https://github.com/Sshanu/AdaptSegNet,N
learning to adapt structured output space for semantic segmentation,https://github.com/wasidennis/AdaptSegNet,N
curriculum domain adaptation for semantic segmentation of urban scenes,https://github.com/YangZhang4065/AdaptationSeg,Y
multimodal unsupervised image-to-image translation,https://github.com/nvlabs/MUNIT,N
multimodal unsupervised image-to-image translation,https://github.com/timsainb/GAIA,Y
unsupervised image-to-image translation networks,https://github.com/SoonminHwang/rgbt-ped-detection,N
unsupervised image-to-image translation networks,https://github.com/huicongzhang/UNIT,N
unsupervised image-to-image translation networks,https://github.com/mingyuliutw/UNIT,N
toward multimodal image-to-image translation,https://github.com/junyanz/BicycleGAN,N
toward multimodal image-to-image translation,https://github.com/manicman1999/Sword-GAN32,N
deep speech 2: end-to-end speech recognition in english and mandarin,https://github.com/UnofficialJuliaMirror/DeepMark-deepmark,Y
deep speech 2: end-to-end speech recognition in english and mandarin,https://github.com/baidu-research/warp-ctc,Y
deep speech 2: end-to-end speech recognition in english and mandarin,https://github.com/UnofficialJuliaMirrorSnapshots/DeepMark-deepmark,Y
deep speech 2: end-to-end speech recognition in english and mandarin,https://github.com/SeanNaren/deepspeech.torch,N
deep speech 2: end-to-end speech recognition in english and mandarin,https://github.com/SeanNaren/deepspeech.pytorch,N
deep speech 2: end-to-end speech recognition in english and mandarin,https://github.com/myrtleSoftware/deepspeech,Y
deep speech 2: end-to-end speech recognition in english and mandarin,https://github.com/noahchalifour/deepspeech2-tensorflow,Y
deep speech 2: end-to-end speech recognition in english and mandarin,https://github.com/DeepMark/deepmark,Y
deep speech 2: end-to-end speech recognition in english and mandarin,https://github.com/tensorflow/models/tree/master/research/deep_speech,Y
deep speech: scaling up end-to-end speech recognition,https://github.com/myrtleSoftware/deepspeech,Y
deep speech: scaling up end-to-end speech recognition,https://github.com/IBM/MAX-Speech-to-Text-Converter,Y
deep speech: scaling up end-to-end speech recognition,https://github.com/mozilla/DeepSpeech,Y
building state-of-the-art distant speech recognition using the chime-4 challenge with a setup of speech enhancement baseline,https://github.com/kaldi-asr/kaldi,N
the pytorch-kaldi speech recognition toolkit,https://github.com/xpz123/pytorch-kaldi,Y
the pytorch-kaldi speech recognition toolkit,https://github.com/mravanelli/pytorch-kaldi,Y
attention-based models for speech recognition,https://github.com/CKRC24/Listen-and-Translate,Y
quaternion convolutional neural networks for end-to-end automatic speech recognition,https://github.com/Orkis-Research/Pytorch-Quaternion-Neural-Networks,N
online and linear-time attention by enforcing monotonic alignments,https://github.com/craffel/mad,Y
neural network language modeling with letter-based features and importance sampling,https://github.com/kaldi-asr/kaldi,N
jasper: an end-to-end convolutional neural acoustic model,https://github.com/NVIDIA/OpenSeq2Seq,N
snips voice platform: an embedded spoken language understanding system for private-by-design voice interfaces,https://github.com/snipsco/nlu-benchmark,N
snips voice platform: an embedded spoken language understanding system for private-by-design voice interfaces,https://github.com/snipsco/snips-nlu,N
purely sequence-trained neural networks for asr based on lattice-free mmi,https://github.com/kaldi-asr/kaldi,N
end-to-end speech recognition using lattice-free mmi,https://github.com/kaldi-asr/kaldi,N
voicefilter: targeted voice separation by speaker-conditioned spectrogram masking,https://github.com/mindslab-ai/voicefilter,N
improved training of end-to-end attention models for speech recognition,https://github.com/rwth-i6/returnn-experiments,N
improved training of end-to-end attention models for speech recognition,https://github.com/rwth-i6/returnn,N
letter-based speech recognition with gated convnets,https://github.com/MrMao/wav2letter,Y
letter-based speech recognition with gated convnets,https://github.com/eric-erki/wav2letter,Y
letter-based speech recognition with gated convnets,https://github.com/9dwLab/Wav2Letter,Y
multilingual part-of-speech tagging with bidirectional long short-term memory models and auxiliary loss,https://github.com/bplank/bilstm-aux,N
a novel neural network model for joint pos tagging and graph-based dependency parsing,https://github.com/datquocnguyen/jPTDP,N
morphosyntactic tagging with a meta-bilstm model over context sensitive token encodings,https://github.com/google/meta_tagger,N
contextual string embeddings for sequence labeling,https://github.com/zalandoresearch/flair,Y
finding function in form: compositional character models for open vocabulary word representation,https://github.com/wlin12/JNN,N
transfer learning for sequence tagging with hierarchical recurrent networks,https://github.com/jiesutd/PyTorchSeqLabel,N
transfer learning for sequence tagging with hierarchical recurrent networks,https://github.com/kimiyoung/transfer,N
transfer learning for sequence tagging with hierarchical recurrent networks,https://github.com/jiesutd/NCRFpp,N
end-to-end sequence labeling via bi-directional lstm-cnns-crf,https://github.com/gump88/AttenCWS,N
end-to-end sequence labeling via bi-directional lstm-cnns-crf,https://github.com/epwalsh/pytorch-crf,Y
end-to-end sequence labeling via bi-directional lstm-cnns-crf,https://github.com/uahmad235/NER-Deep-Learning,N
end-to-end sequence labeling via bi-directional lstm-cnns-crf,https://github.com/IBM/MAX-Named-Entity-Tagger,Y
end-to-end sequence labeling via bi-directional lstm-cnns-crf,https://github.com/SNUDerek/multiLSTM,Y
end-to-end sequence labeling via bi-directional lstm-cnns-crf,https://github.com/gpandu/NER_DNN,Y
end-to-end sequence labeling via bi-directional lstm-cnns-crf,https://github.com/guillaumegenthial/sequence_tagging,Y
end-to-end sequence labeling via bi-directional lstm-cnns-crf,https://github.com/XiafeiYu/CNN_BILSTM_CRF,Y
end-to-end sequence labeling via bi-directional lstm-cnns-crf,https://github.com/gitzgk/nlp-beginner,Y
end-to-end sequence labeling via bi-directional lstm-cnns-crf,https://github.com/sz128/Natural-language-understanding-papers,N
sentence-state lstm for text representation,https://github.com/leuchine/S-LSTM,N
empower sequence labeling with task-aware neural language model,https://github.com/zysite/post,Y
empower sequence labeling with task-aware neural language model,https://github.com/LiyuanLucasLiu/LM-LSTM-CRF,Y
ncrf++: an open-source neural sequence labeling toolkit,https://github.com/jiesutd/PyTorchSeqLabel,Y
ncrf++: an open-source neural sequence labeling toolkit,https://github.com/jiesutd/NCRFpp,Y
semi-supervised multitask learning for sequence labeling,https://github.com/marekrei/sequence-labeler,Y
multiresolution recurrent neural networks: an application to dialogue response generation,https://github.com/julianser/Ubuntu-Multiresolution-Tools,N
multiresolution recurrent neural networks: an application to dialogue response generation,https://github.com/WolfNiu/AdversarialDialogue,N
multiresolution recurrent neural networks: an application to dialogue response generation,https://github.com/julianser/hed-dlg-truncated,N
towards universal dialogue state tracking,https://github.com/renll/StateNet,N
global-locally self-attentive dialogue state tracker,https://github.com/salesforce/glad,N
dialogue learning with human teaching and feedback in end-to-end trainable task-oriented dialogue systems,https://github.com/google-research-datasets/simulated-dialogue,N
dialogue act sequence labeling using hierarchical encoder with crf,https://github.com/ilimugur/short-text-classification,N
hypernyms under siege: linguistically-motivated artillery for hypernymy detection,https://github.com/vered1986/UnsupervisedHypernymy,N
parameter-free spatial attention network for person re-identification,https://github.com/schizop/SA,Y
parameter-free spatial attention network for person re-identification,https://github.com/HRanWang/SA,Y
parameter-free spatial attention network for person re-identification,https://github.com/HRanWang/Spatial-Attention,Y
learning discriminative features with multiple granularities for person re-identification,https://github.com/LcenArthas/Kaggle-Humpback-Whale-Identification,N
learning discriminative features with multiple granularities for person re-identification,https://github.com/lwplw/reid-mgn,N
learning discriminative features with multiple granularities for person re-identification,https://github.com/joehammer934/MGN-ReId,N
learning discriminative features with multiple granularities for person re-identification,https://github.com/zp1018/ReID-MGN,N
learning discriminative features with multiple granularities for person re-identification,https://github.com/kilsenp/triplet-reid-pytorch,N
learning discriminative features with multiple granularities for person re-identification,https://github.com/seathiefwang/MGN-pytorch,N
learning discriminative features with multiple granularities for person re-identification,https://github.com/GNAYUOHZ/ReID-MGN,N
learning discriminative features with multiple granularities for person re-identification,https://github.com/lwplw/re-id_mgn,N
beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline),https://github.com/syfafterzy/PCB_RPP,Y
beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline),https://github.com/layumi/Person_reID_baseline_pytorch,N
beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline),https://github.com/HoganZhang/Person_reID_baseline_pytorch,N
beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline),https://github.com/NIRVANALAN/reid_baseline,N
beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline),https://github.com/xuxu116/pytorch-reid-lite,N
beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline),https://github.com/GuHongyang/Person-ReID-Pytorch,N
random erasing data augmentation,https://github.com/rickyHong/Random-erasing-Data-Augumentation-repl,Y
random erasing data augmentation,https://github.com/aditya30394/Person-Re-Identification,Y
random erasing data augmentation,https://github.com/zhunzhong07/Random-Erasing,Y
incremental learning in person re-identification,https://github.com/prajjwal1/person-reid-incremental,N
camera style adaptation for person re-identification,https://github.com/layumi/Person_reID_baseline_pytorch,Y
camera style adaptation for person re-identification,https://github.com/HoganZhang/Person_reID_baseline_pytorch,Y
camera style adaptation for person re-identification,https://github.com/NIRVANALAN/reid_baseline,Y
in defense of the triplet loss for person re-identification,https://github.com/agongt408/vbranch,N
in defense of the triplet loss for person re-identification,https://github.com/cftang0827/human_recognition,Y
in defense of the triplet loss for person re-identification,https://github.com/BarbaAlGhul/recommender-system-thesis,N
in defense of the triplet loss for person re-identification,https://github.com/lyakaap/NetVLAD-pytorch,Y
in defense of the triplet loss for person re-identification,https://github.com/thomas-liao/diva_tracking_reid,Y
in defense of the triplet loss for person re-identification,https://github.com/infinitylogesh/Kaggle-Quora-Incincere-question-classification,Y
in defense of the triplet loss for person re-identification,https://github.com/cftang0827/pedestrian_recognition,Y
in defense of the triplet loss for person re-identification,https://github.com/VisualComputingInstitute/triplet-reid,Y
in defense of the triplet loss for person re-identification,https://github.com/IcewineChen/mxnet-batch_hard_triplet_loss,Y
in defense of the triplet loss for person re-identification,https://github.com/kilsenp/triplet-reid-pytorch,Y
in defense of the triplet loss for person re-identification,https://github.com/h4veFunCodin9/Aligned_ReID,Y
in defense of the triplet loss for person re-identification,https://github.com/0b3d/loc2map,Y
in defense of the triplet loss for person re-identification,https://github.com/abhishirk/Aligned_ReId,Y
in defense of the triplet loss for person re-identification,https://github.com/AsuradaYuci/tripletreid-zhushi,Y
in defense of the triplet loss for person re-identification,https://github.com/giovanniguidi/face_recognition-tf,Y
in defense of the triplet loss for person re-identification,https://github.com/huanghoujing/AlignedReID-Re-Production-Pytorch,Y
pedestrian alignment network for large-scale person re-identification,https://github.com/layumi/Pedestrian_Alignment,Y
joint detection and identification feature learning for person search,https://github.com/ShuangLI59/person_search,Y
unlabeled samples generated by gan improve the person re-identification baseline in vitro,https://github.com/layumi/DukeMTMC-reID_evaluation,N
unlabeled samples generated by gan improve the person re-identification baseline in vitro,https://github.com/layumi/Person-reID_GAN,Y
attribute-aware attention model for fine-grained representation learning,https://github.com/iamhankai/attribute-aware-attention,Y
deeply-learned part-aligned representations for person re-identification,https://github.com/Phoebe-star/part_aligned,Y
a discriminatively learned cnn embedding for person re-identification,https://github.com/D-X-Y/caffe-reid,N
a discriminatively learned cnn embedding for person re-identification,https://github.com/layumi/2016_person_re-ID,Y
a discriminatively learned cnn embedding for person re-identification,https://github.com/LDVC124/2016_person_re-ID,Y
horizonnet: learning room layout with 1d representation and pano stretch data augmentation,https://github.com/sunset1995/HorizonNet,N
corners for layout: end-to-end layout recovery from 360 images,https://github.com/cfernandezlab/CFL,N
layoutnet: reconstructing the 3d room layout from a single rgb image,https://github.com/zouchuhang/LayoutNet,N
layoutnet: reconstructing the 3d room layout from a single rgb image,https://github.com/sunset1995/pytorch-layoutnet,N
scan2cad: learning cad model alignment in rgb-d scans,https://github.com/skanti/Scan2CAD,Y
scan2cad: learning cad model alignment in rgb-d scans,https://github.com/skanti/Scan2CAD-Annotation-Webapp,Y
3dmatch: learning local geometric descriptors from rgb-d reconstructions,https://github.com/andyzeng/3dmatch-toolbox,Y
fast online object tracking and segmentation: a unifying approach,https://github.com/foolwood/SiamMask,N
learning spatial-temporal regularized correlation filters for visual tracking,https://github.com/lifeng9472/STRCF,N
distractor-aware siamese networks for visual object tracking,https://github.com/foolwood/DaSiamRPN,N
eco: efficient convolution operators for tracking,https://github.com/martin-danelljan/ECO,N
discriminative correlation filter with channel and spatial reliability,https://github.com/alanlukezic/csr-dcf,N
high performance visual tracking with siamese region proposal network,https://github.com/zkisthebest/Siamese-RPN,N
high performance visual tracking with siamese region proposal network,https://github.com/foolwood/DaSiamRPN,N
temporal relational reasoning in videos,https://github.com/okankop/MFF-pytorch,Y
temporal relational reasoning in videos,https://github.com/metalbubble/TRN-pytorch,Y
"quo vadis, action recognition? a new model and the kinetics dataset",https://github.com/coderSkyChen/Action_Recognition_Zoo,Y
"quo vadis, action recognition? a new model and the kinetics dataset",https://github.com/ahsaniqbal/Kinetics-FeatureExtractor,Y
"quo vadis, action recognition? a new model and the kinetics dataset",https://github.com/sebastiantiesmeyer/deeplabchop3d,N
"quo vadis, action recognition? a new model and the kinetics dataset",https://github.com/deepmind/kinetics-i3d,Y
"quo vadis, action recognition? a new model and the kinetics dataset",https://github.com/vijayvee/behavior-recognition,Y
"quo vadis, action recognition? a new model and the kinetics dataset",https://github.com/vijayvee/behavior_recognition,Y
"quo vadis, action recognition? a new model and the kinetics dataset",https://github.com/FrederikSchorr/sign-language,Y
"quo vadis, action recognition? a new model and the kinetics dataset",https://github.com/piergiaj/pytorch-i3d,Y
"quo vadis, action recognition? a new model and the kinetics dataset",https://github.com/yaohungt/GSTEG_CVPR_2019,Y
residual dense network for image restoration,https://github.com/yulunzhang/RDN,Y
ffdnet: toward a fast and flexible solution for cnn based image denoising,https://github.com/cszn/FFDNet,N
learning deep cnn denoiser prior for image restoration,https://github.com/cszn/ircnn,Y
image restoration using convolutional auto-encoders with symmetric skip connections,https://github.com/ved27/RED-net,Y
image restoration using convolutional auto-encoders with symmetric skip connections,https://github.com/jupiterman/Super-Resolution-Images,N
image restoration using convolutional auto-encoders with symmetric skip connections,https://github.com/titu1994/Image-Super-Resolution,N
multi-task learning as multi-objective optimization,https://github.com/IntelVCL/MultiObjectiveOptimization,N
easy transfer learning by exploiting intra-domain structures,https://github.com/jindongwang/transferlearning,N
learning to compare: relation network for few-shot learning,https://github.com/flexibility2/Pytorch-Implementation-for-RelationNet,Y
learning to compare: relation network for few-shot learning,https://github.com/knnaraghi/fewshot,Y
learning to compare: relation network for few-shot learning,https://github.com/floodsung/LearningToCompare_FSL,Y
learning to compare: relation network for few-shot learning,https://github.com/lzrobots/LearningToCompare_ZSL,Y
meta-transfer learning for few-shot learning,https://github.com/y2l/meta-transfer-learning-tensorflow,Y
denoising distantly supervised open-domain question answering,https://github.com/thunlp/OpenQA,N
densely connected attention propagation for reading comprehension,https://github.com/vanzytay/NIPS2018_DECAPROP,Y
gated-attention readers for text comprehension,https://github.com/bdhingra/ga-reader,Y
gated-attention readers for text comprehension,https://github.com/aartika/experiment1,Y
bidirectional attention flow for machine comprehension,https://github.com/baidu/DuReader,Y
bidirectional attention flow for machine comprehension,https://github.com/allenai/bi-att-flow,Y
bidirectional attention flow for machine comprehension,https://github.com/tikyau/bidaf-quick-test,Y
bidirectional attention flow for machine comprehension,https://github.com/Red-Inkstone/dbqa,Y
bidirectional attention flow for machine comprehension,https://github.com/ghus75/Question_Answering,Y
bidirectional attention flow for machine comprehension,https://github.com/andrejonasson/dynamic-coattention-network-plus,Y
bidirectional attention flow for machine comprehension,https://github.com/lmn-extracts/dcn_plus,Y
bidirectional attention flow for machine comprehension,https://github.com/GauthierDmn/question_answering,Y
bidirectional attention flow for machine comprehension,https://github.com/Deepak-Work/Passage_Retrieval,Y
bidirectional attention flow for machine comprehension,https://github.com/JaonM/MachineComprehension,Y
bidirectional attention flow for machine comprehension,https://github.com/techit-limtiyakul/bidirectional-attention-flow,Y
bidirectional attention flow for machine comprehension,https://github.com/Shravsridhar/QA-Model-INFO-7374,Y
a question-focused multi-factor attention network for question answering,https://github.com/nusnlp/amanda,Y
text understanding with the attention sum reader network,https://github.com/rkadlec/asreader,Y
text understanding with the attention sum reader network,https://github.com/shuxiaobo/QA-Experiment,Y
text understanding with the attention sum reader network,https://github.com/libertatis/mrc-cbt,Y
modeling semantics with gated graph neural networks for knowledge base question answering,https://github.com/UKPLab/coling2018-graph-neural-networks-question-answering,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/tianxin1860/Papers,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/lonePatient/BERT-chinese-text-classification-pytorch,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/guoyaohua/BERT-Chinese-Annotation,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/TSLNIHAOGIT/bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/Laura1021678398/CCF-Bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/SYangDong/bert-with-frozen-code,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/Nstats/bert_MRC,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/BirgerMoell/bertcastle,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/Satan012/BERT,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/lkfo415579/MT-Readling-List,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/semal/bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/xiaopingzhong/bert-finetune-for-classfier,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/svakulenk0/response_eval,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/socc-io/piqaboo,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/whqwill/seq2seq-keyphrase-bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/mayurnewase/Quora-Bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/rpuiggari/bert2,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/MichaelZhouwang/LMlexsub,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/eyb1/alpha,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/jageshmaharjan/BERT_Service,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/benywon/ChineseBert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/pfecht/bert-exploration,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/woonzh/semantics,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/mflotynski/ninja-bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/xuhuimin2017/bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/Kosuke-Szk/BERT-NER-ja,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/fabiocorreacordeiro/Elsevier_abstracts-Classification,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/jsalt18-sentence-repl/jiant,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/LoveYang/bert_test,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/wayalhruhi/gogle_bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/TSLNIHAOGIT/bert_run,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/goldenbili/Bert_Test3,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/dmlc/gluon-nlp,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/lovedavidsilva/bert_old_version,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/zhongyunuestc/bert_multitask,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/shaikhzhas/bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/icewing1996/bert_dep,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/zapplea/bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/Colanim/BERT_STS-B,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/codertimo/BERT-pytorch,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/zsweet/BERT_zsw,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/graykode/nlp-tutorial,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/longbowking/bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/lonePatient/Bert-Multi-Label-Text-Classification,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/freedombenLiu/bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/nmfisher/bert-modified,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/jiyuan/ainote,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/kingcheng2000/bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/Doffery/BERT-Sentiment-Analysis-Amazon-Review,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/lonePatient/bert-sentence-similarity-pytorch,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/circlePi/BERT_Chinese_Text_Class_By_pytorch,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/saurabhgupta17888/BERT,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/saurabhnlp/bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/linboqiao/BERT,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/yydai/bert_test,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/tyxr/bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/egyptdj/bert-extractor,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/arvieFrydenlund/bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/guzhang480/Google_BERT,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/Zeeshan75/Bert_Telugu_Ner,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/gxdalu-yaya/bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/layziezz/tesuto,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/xbtlin/All-about-Machine-Learning,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/chen-xiong-yi/OwnBERT,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/DandyQi/bert-learning,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/myamamoto555/tf-bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/GauthierDmn/question_answering,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/MaZhiyuanBUAA/bert-tf1.4.0,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/tcnguyen/bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/chuanbanjun/bert_comment,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/SkullFang/BERT_NLP_Classification,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/vijay120/bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/YYGXjpg/BERT_WL,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/liqifyl/google-bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/google-research/bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/yiyc-kor/bert-study,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/felix-do-wizardry/bert_felix,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/kamalkraj/BERT_NER,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/BroCoLySTyLe/SQLovaReview,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/enod/arxiv-nlp-notes,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/Y1ran/NLP-BERT--ChineseVersion,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/tangmingyi/BERT,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/huggingface/pytorch-pretrained-BERT,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/viniciusoliveirasd/bert-applications,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/brightmart/text_classification,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/kyzhouhzau/BERT-NER,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/goldenbili/Bert_Test2,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/JohannLee1996/bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/luckynozomi/PPI_Bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/huoyanfeilikesi/GOOGLE-BERT,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/kinimod23/NMT_Project,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/yanzhitech/bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/xesdiny/test-bert-master,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/DeokO/bert-excercise-ongoing,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/Gaozhen0816/BERT_QA_For_AILaw,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/fyang623/QANTA-Project,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/saurabhkulkarni77/BERT_multilabel,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/NathanDuran/BERT,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/frankcgq105/BERTCHEN,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/pengming617/bert_classification,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/fciannel/bert_fciannel,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/Kevin-Vora/bert-embedding-gluonnlp-edit-,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/Nstats/my_bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/naver/sqlova,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/sz128/Natural-language-understanding-papers,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/why2000/DuReader-bert,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/jangjoongkeon/JK,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/IBM/MAX-Text-Sentiment-Classifier,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/dreamgonfly/BERT-pytorch,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/brightmart/bert_customized,N
bert: pre-training of deep bidirectional transformers for language understanding,https://github.com/chiayewken/bert-qa,N
a parallel-hierarchical model for machine comprehension on sparse data,https://github.com/Maluuba/mctest-model,Y
large-scale simple question answering with memory networks,https://github.com/facebookresearch/ParlAI,N
hyperbolic representation learning for fast and efficient neural question answering,https://github.com/vanzytay/WSDM2018_HyperQA,N
deep learning for answer sentence selection,https://github.com/brmson/dataset-sts,Y
deep learning for answer sentence selection,https://github.com/umutguneri/Question-Answering-Assistant,Y
reinforced mnemonic reader for machine reading comprehension,https://github.com/yly-revive/chainer-mreader,Y
reinforced mnemonic reader for machine reading comprehension,https://github.com/DeepInEvil/matchLSTMtrained,Y
reinforced mnemonic reader for machine reading comprehension,https://github.com/ewrfcas/Reinforced-Mnemonic-Reader,Y
deep contextualized word representations,https://github.com/yuanxiaosc/ELMo,Y
deep contextualized word representations,https://github.com/tianxin1860/Papers,N
deep contextualized word representations,https://github.com/PrashantRanjan09/Elmo-Tutorial,Y
deep contextualized word representations,https://github.com/cheng18/bilm-tf,Y
deep contextualized word representations,https://github.com/PrashantRanjan09/WordEmbeddings-Elmo-Fasttext-Word2Vec,Y
deep contextualized word representations,https://github.com/codertimo/ELMO-tf,N
deep contextualized word representations,https://github.com/dmlc/gluon-nlp,N
deep contextualized word representations,https://github.com/YC-wind/embedding_study,Y
deep contextualized word representations,https://github.com/memoryjing/bilm-tf-master,Y
deep contextualized word representations,https://github.com/zalandoresearch/flair,Y
deep contextualized word representations,https://github.com/iliaschalkidis/ELMo-keras,Y
deep contextualized word representations,https://github.com/Hironsan/anago,Y
deep contextualized word representations,https://github.com/kinimod23/NMT_Project,N
deep contextualized word representations,https://github.com/SammyVimes/ELMo-fork,Y
deep contextualized word representations,https://github.com/TEAMLAB-Lecture/deep_nlp_101,Y
deep contextualized word representations,https://github.com/yuanjing-zhu/elmo,Y
deep contextualized word representations,https://github.com/UKPLab/elmo-bilstm-cnn-crf,Y
deep contextualized word representations,https://github.com/HIT-SCIR/ELMoForManyLangs,Y
deep contextualized word representations,https://github.com/zhangyuwangumass/Jointly-Learning-Remote-Relation-with-ELMo,Y
deep contextualized word representations,https://github.com/eyb1/alpha,N
deep contextualized word representations,https://github.com/Ismailhachimi/Text-Classification,N
deep contextualized word representations,https://github.com/JHart96/keras_elmo_embedding_layer,Y
deep contextualized word representations,https://github.com/yangzonglin1994/bilm-tf-extended,Y
deep contextualized word representations,https://github.com/menajosep/AleatoricSent,Y
deep contextualized word representations,https://github.com/why2011btv/FB15k237_junnan,Y
deep contextualized word representations,https://github.com/shelleyHLX/bilm_EMLo,Y
deep contextualized word representations,https://github.com/allenai/bilm-tf,Y
deep contextualized word representations,https://github.com/SeonbeomKim/TensorFlow-ELMo,Y
deep contextualized word representations,https://github.com/nlp-research/bilm-tf,N
stochastic answer networks for machine reading comprehension,https://github.com/kevinduh/san_mrc,Y
stochastic answer networks for machine reading comprehension,https://github.com/ks-korovina/my_san,Y
stochastic answer networks for machine reading comprehension,https://github.com/Srinivas-R/Squad,N
stochastic answer networks for machine reading comprehension,https://github.com/lduml/blog,Y
stochastic answer networks for machine reading comprehension,https://github.com/Xaniar87/SAN_SQuAD2,Y
fusionnet: fusing via fully-aware attention with application to machine comprehension,https://github.com/felixgwu/FastFusionNet,N
fusionnet: fusing via fully-aware attention with application to machine comprehension,https://github.com/momohuang/FusionNet-NLI,N
dcn+: mixed objective and deep residual coattention for question answering,https://github.com/andrejonasson/dynamic-coattention-network-plus,N
dcn+: mixed objective and deep residual coattention for question answering,https://github.com/lmn-extracts/dcn_plus,N
contextualized word representations for reading comprehension,https://github.com/shimisalant/CWR,Y
qanet: combining local convolution with global self-attention for reading comprehension,https://github.com/annaorosz/my_qanet_implementation,N
qanet: combining local convolution with global self-attention for reading comprehension,https://github.com/ajoshi80/QANet,N
qanet: combining local convolution with global self-attention for reading comprehension,https://github.com/Tao-Mind/QDD_Net,N
qanet: combining local convolution with global self-attention for reading comprehension,https://github.com/ewrfcas/QANet_keras,N
qanet: combining local convolution with global self-attention for reading comprehension,https://github.com/JaonM/MachineComprehension,N
qanet: combining local convolution with global self-attention for reading comprehension,https://github.com/NLPLearn/QANet,N
qanet: combining local convolution with global self-attention for reading comprehension,https://github.com/yuriak/PPDAI,N
simple and effective multi-paragraph reading comprehension,https://github.com/allenai/document-qa,Y
dynamic coattention networks for question answering,https://github.com/Hapiny/dcn,Y
dynamic coattention networks for question answering,https://github.com/BAJUKA/SQuAD-NLP,Y
dynamic coattention networks for question answering,https://github.com/wasimusu/MachineRC,Y
dynamic coattention networks for question answering,https://github.com/andrejonasson/dynamic-coattention-network-plus,Y
dynamic coattention networks for question answering,https://github.com/Lou1sM/AML-Project,Y
dynamic coattention networks for question answering,https://github.com/lmn-extracts/dcn_plus,Y
learned in translation: contextualized word vectors,https://github.com/salesforce/cove,Y
learned in translation: contextualized word vectors,https://github.com/menajosep/AleatoricSent,Y
learned in translation: contextualized word vectors,https://github.com/adi2103/AML-CoVe,Y
making neural qa as simple as possible but not simpler,https://github.com/uclmr/jack,Y
learning recurrent span representations for extractive question answering,https://github.com/shimisalant/RaSoR,N
reading wikipedia to answer open-domain questions,https://github.com/facebookresearch/ParlAI,N
reading wikipedia to answer open-domain questions,https://github.com/hitvoice/DrQA,N
reading wikipedia to answer open-domain questions,https://github.com/facebookresearch/DrQA,Y
reading wikipedia to answer open-domain questions,https://github.com/BAJUKA/SQuAD-NLP,Y
machine comprehension using match-lstm and answer pointer,https://github.com/baidu/DuReader,Y
machine comprehension using match-lstm and answer pointer,https://github.com/shuohangwang/SeqMatchSeq,Y
machine comprehension using match-lstm and answer pointer,https://github.com/DeepInEvil/matchLSTMtrained,Y
machine comprehension using match-lstm and answer pointer,https://github.com/geraltofrivia/match-lstm-ptr-network,Y
machine comprehension using match-lstm and answer pointer,https://github.com/HKUST-KnowComp/R-Net,Y
words or characters? fine-grained gating for reading comprehension,https://github.com/kimiyoung/fg-gating,N
commonsense for generative multi-hop question answering tasks,https://github.com/yicheng-w/CommonSenseMultiHopQA,Y
query-reduction networks for question answering,https://github.com/uwnlp/qrn,Y
tracking the world state with recurrent entity networks,https://github.com/siddk/entity-network,Y
tracking the world state with recurrent entity networks,https://github.com/facebook/MemNN,Y
tracking the world state with recurrent entity networks,https://github.com/AlgorTroy/Recurrent-Entity-Network-EntNet,Y
end-to-end memory networks,https://github.com/TDeepanshPandey/Chat_Bot,Y
end-to-end memory networks,https://github.com/simonjisu/E2EMN,Y
end-to-end memory networks,https://github.com/raunaks13/MemN2Ns,Y
end-to-end memory networks,https://github.com/cstghitpku/GateMemN2N,Y
end-to-end memory networks,https://github.com/facebook/bAbI-tasks,Y
end-to-end memory networks,https://github.com/uwnlp/qrn,Y
end-to-end memory networks,https://github.com/ishalyminov/memn2n,Y
end-to-end memory networks,https://github.com/dare0021/MemN2N_Bench,Y
end-to-end memory networks,https://github.com/facebook/MemNN,Y
end-to-end memory networks,https://github.com/stikbuf/Language_Modeling,Y
end-to-end memory networks,https://github.com/umutguneri/Question-Answering-Assistant,Y
end-to-end memory networks,https://github.com/nbansal90/bAbi_QA,Y
end-to-end memory networks,https://github.com/thomlake/pytorch-attention,Y
end-to-end memory networks,https://github.com/domluna/memn2n,Y
end-to-end memory networks,https://github.com/huiyinglu/NLP-Deep-Learning-Chatbot,Y
end-to-end memory networks,https://github.com/prashil2792/Question-Answering-System-Deep-Learning,Y
end-to-end memory networks,https://github.com/aadil-srivastava01/End-To-End-Memory-Networks,Y
end-to-end memory networks,https://github.com/sy-sunmoon/Clever-Commenter-Let-s-Try-More-Apps,Y
end-to-end memory networks,https://github.com/facebook/MazeBase,Y
end-to-end memory networks,https://github.com/harvardnlp/struct-attn,N
end-to-end memory networks,https://github.com/rakeshbm/QA-using-Torch,N
end-to-end memory networks,https://github.com/sheryl-ai/MemGCN,Y
end-to-end memory networks,https://github.com/SeonbeomKim/TensorFlow-MemN2N,Y
end-to-end memory networks,https://github.com/gryan12/chat-bot,Y
end-to-end memory networks,https://github.com/vinhkhuc/MemN2N-babi-python,Y
rotational unit of memory,https://github.com/jingli9111/RUM-Tensorflow,Y
gated orthogonal recurrent units: on learning to forget,https://github.com/jingli9111/GORU-tensorflow,Y
recurrent relational networks,https://github.com/Kyubyong/sudoku,Y
recurrent relational networks,https://github.com/AndreaCossu/Relation-Network-PyTorch,Y
recurrent relational networks,https://github.com/rasmusbergpalm/recurrent-relational-networks,N
neural variational inference for text processing,https://github.com/jainshobhit/Variational-Autoencoder,Y
neural variational inference for text processing,https://github.com/ysmiao/nvdm,Y
neural variational inference for text processing,https://github.com/jiacheng-xu/vmf_vae_nlp,Y
neural variational inference for text processing,https://github.com/carpedm20/variational-text-tensorflow,Y
distributed representations of sentences and documents,https://github.com/Antonildo43/Classifica-o-de-textos-com-doc2Vec,Y
distributed representations of sentences and documents,https://github.com/slme1109/lyrics-generator,Y
distributed representations of sentences and documents,https://github.com/bombdiggity/paper-bag,Y
distributed representations of sentences and documents,https://github.com/dhyeon/ingredient-vectors,Y
distributed representations of sentences and documents,https://github.com/kitnhl/partisan-tweets-classification,Y
distributed representations of sentences and documents,https://github.com/ibrahimsharaf/doc2vec,N
distributed representations of sentences and documents,https://github.com/DCYN/Ramdomized-Clinical-Trail-Classification,N
distributed representations of sentences and documents,https://github.com/inejc/paragraph-vectors,Y
distributed representations of sentences and documents,https://github.com/YinpeiDai/NAUM,N
distributed representations of sentences and documents,https://github.com/tsandefer/capstone_2,Y
distributed representations of sentences and documents,https://github.com/kr900910/supreme_court_opinion,N
distributed representations of sentences and documents,https://github.com/kramamur/sentiment-analysis,N
distributed representations of sentences and documents,https://github.com/fabiocorreacordeiro/Elsevier_abstracts-Classification,N
distributed representations of sentences and documents,https://github.com/rvstraalen/doc2vec-workshop,Y
distributed representations of sentences and documents,https://github.com/eske/multivec,Y
distributed representations of sentences and documents,https://github.com/kinimod23/NMT_Project,N
sdnet: contextualized attention-based deep network for conversational question answering,https://github.com/mpandeydev/SDnetmod,N
sdnet: contextualized attention-based deep network for conversational question answering,https://github.com/Microsoft/SDNet,N
flowqa: grasping flow in history for conversational machine comprehension,https://github.com/momohuang/FlowQA,N
"a qualitative comparison of coqa, squad 2.0 and quac",https://github.com/my89/co-squac,N
coqa: a conversational question answering challenge,https://github.com/WERimagin/baseline_CoQA,Y
coqa: a conversational question answering challenge,https://github.com/mrzjy/sunburst,Y
coqa: a conversational question answering challenge,https://github.com/stanfordnlp/coqa-baselines,Y
coqa: a conversational question answering challenge,https://github.com/chiahsuan156/Question-Answering_Resources_Papers,Y
convolutional neural network architectures for matching natural language sentences,https://github.com/Elvirasun28/quora-question-duplicate,N
convolutional neural network architectures for matching natural language sentences,https://github.com/SJHBXShub/Question_pair,N
unimelb at semeval-2016 tasks 4a and 4b: an ensemble of neural networks and a word2vec based model for sentiment classification,https://github.com/liufly/narrative-modeling,N
read + verify: machine reading comprehension with unanswerable questions,https://github.com/yujiakimoto/mnemonic-reader,Y
a thorough examination of the cnn/daily mail reading comprehension task,https://github.com/danqi/rc-cnn-dailymail,Y
iterative alternating neural attention for machine reading,https://github.com/AI-metrics/AI-metrics,Y
attention-over-attention neural networks for reading comprehension,https://github.com/shuxiaobo/QA-Experiment,Y
attention-over-attention neural networks for reading comprehension,https://github.com/Red-Inkstone/dbqa,Y
teaching machines to read and comprehend,https://github.com/facebookresearch/ParlAI,Y
teaching machines to read and comprehend,https://github.com/shuxiaobo/QA-Experiment,N
teaching machines to read and comprehend,https://github.com/deepmind/rc-data,Y
the narrativeqa reading comprehension challenge,https://github.com/chiahsuan156/Question-Answering_Resources_Papers,Y
language models are unsupervised multitask learners,https://github.com/openai/gpt-2,Y
language models are unsupervised multitask learners,https://github.com/minimaxir/gpt-2-simple,N
key-value memory networks for directly reading documents,https://github.com/facebookresearch/ParlAI,N
key-value memory networks for directly reading documents,https://github.com/jojonki/key-value-memory-networks,Y
sentence similarity learning by lexical decomposition and composition,https://github.com/Leputa/CIKM-AnalytiCup-2018,Y
improving relation extraction by pre-trained language representations,https://github.com/DFKI-NLP/TRE,Y
tips and tricks for visual question answering: learnings from the 2017 challenge,https://github.com/SinghJasdeep/Attention-on-Attention-for-VQA,N
tips and tricks for visual question answering: learnings from the 2017 challenge,https://github.com/hengyuan-hu/bottom-up-attention-vqa,N
tips and tricks for visual question answering: learnings from the 2017 challenge,https://github.com/feifengwhu/question_attention,N
tips and tricks for visual question answering: learnings from the 2017 challenge,https://github.com/VincentYing/Attention-on-Attention-for-VQA,N
tips and tricks for visual question answering: learnings from the 2017 challenge,https://github.com/peteanderson80/bottom-up-attention,N
pythia v0.1: the winning entry to the vqa challenge 2018,https://github.com/gabegrand/adversarial-vqa,Y
pythia v0.1: the winning entry to the vqa challenge 2018,https://github.com/songhe17/pythia-clone,Y
pythia v0.1: the winning entry to the vqa challenge 2018,https://github.com/facebookresearch/pythia,Y
mutan: multimodal tucker fusion for visual question answering,https://github.com/gabegrand/adversarial-vqa,N
mutan: multimodal tucker fusion for visual question answering,https://github.com/Cadene/vqa.pytorch,Y
making the v in vqa matter: elevating the role of image understanding in visual question answering,https://github.com/necla-ml/SNLI-VE,N
making the v in vqa matter: elevating the role of image understanding in visual question answering,https://github.com/abhshkdz/neural-vqa-attention,N
modeling relationships in referential expressions with compositional modular networks,https://github.com/hengyuan-hu/bottom-up-attention-vqa,N
multimodal compact bilinear pooling for visual question answering and visual grounding,https://github.com/gabegrand/adversarial-vqa,N
multimodal compact bilinear pooling for visual question answering and visual grounding,https://github.com/akirafukui/vqa-mcb,N
multimodal compact bilinear pooling for visual question answering and visual grounding,https://github.com/Cadene/vqa.pytorch,N
multimodal compact bilinear pooling for visual question answering and visual grounding,https://github.com/jnhwkim/cbp,N
multimodal compact bilinear pooling for visual question answering and visual grounding,https://github.com/MarcBS/keras,N
bottom-up and top-down attention for image captioning and visual question answering,https://github.com/SinghJasdeep/Attention-on-Attention-for-VQA,N
bottom-up and top-down attention for image captioning and visual question answering,https://github.com/Wentong-DST/up-down-captioner,N
bottom-up and top-down attention for image captioning and visual question answering,https://github.com/hengyuan-hu/bottom-up-attention-vqa,N
bottom-up and top-down attention for image captioning and visual question answering,https://github.com/longjj/Caffe-SGDR,N
bottom-up and top-down attention for image captioning and visual question answering,https://github.com/JiahengHu/DeepLearningProject,N
bottom-up and top-down attention for image captioning and visual question answering,https://github.com/feifengwhu/question_attention,N
bottom-up and top-down attention for image captioning and visual question answering,https://github.com/king-zark/self_critical,N
bottom-up and top-down attention for image captioning and visual question answering,https://github.com/ruotianluo/Transformer_Captioning,N
bottom-up and top-down attention for image captioning and visual question answering,https://github.com/VincentYing/Attention-on-Attention-for-VQA,N
bottom-up and top-down attention for image captioning and visual question answering,https://github.com/brandontrabucco/up_down_rnn_cell,N
bottom-up and top-down attention for image captioning and visual question answering,https://github.com/ruotianluo/self-critical.pytorch,N
bottom-up and top-down attention for image captioning and visual question answering,https://github.com/peteanderson80/bottom-up-attention,Y
bottom-up and top-down attention for image captioning and visual question answering,https://github.com/ruotianluo/DiscCaptioning,N
bottom-up and top-down attention for image captioning and visual question answering,https://github.com/brandontrabucco/up_down_cell,N
vqa: visual question answering,https://github.com/tbmoon/basic_vqa,Y
vqa: visual question answering,https://github.com/mishajw/vocab_pie,N
vqa: visual question answering,https://github.com/ramprs/grad-cam,Y
vqa: visual question answering,https://github.com/abhshkdz/neural-vqa-attention,Y
learning to reason: end-to-end module networks for visual question answering,https://github.com/ronghanghu/n2nmn,Y
learning to reason: end-to-end module networks for visual question answering,https://github.com/tensorflow/models/tree/master/research/qa_kg,N
hierarchical question-image co-attention for visual question answering,https://github.com/Rabahjamal/Visual-Question-Answering,Y
hierarchical question-image co-attention for visual question answering,https://github.com/jiasenlu/HieCoAttenVQA,Y
hierarchical question-image co-attention for visual question answering,https://github.com/WillSuen/VQA,Y
stacked attention networks for image question answering,https://github.com/rs9000/VisualReasoning_MMnet,Y
stacked attention networks for image question answering,https://github.com/Cold-Winter/vqs,Y
stacked attention networks for image question answering,https://github.com/zcyang/imageqa-san,Y
stacked attention networks for image question answering,https://github.com/abhshkdz/neural-vqa-attention,Y
simple baseline for visual question answering,https://github.com/metalbubble/VQAbaseline,Y
simple baseline for visual question answering,https://github.com/sidaw/nbsvm,Y
simple baseline for visual question answering,https://github.com/sidgan/whats_in_a_question,Y
predictive business process monitoring with lstm neural networks,https://github.com/verenich/ProcessSequencePrediction,N
massively multilingual sentence embeddings for zero-shot cross-lingual transfer and beyond,https://github.com/facebookresearch/LASER,N
massively multilingual sentence embeddings for zero-shot cross-lingual transfer and beyond,https://github.com/OriginPower/LASER,N
massively multilingual sentence embeddings for zero-shot cross-lingual transfer and beyond,https://github.com/imamathcat/LASER_Dependencies,N
massively multilingual sentence embeddings for zero-shot cross-lingual transfer and beyond,https://github.com/LawrenceDuan/myLASER,N
supervised learning of universal sentence representations from natural language inference data,https://github.com/facebookresearch/InferSent,Y
supervised learning of universal sentence representations from natural language inference data,https://github.com/facebookresearch/SentEval,N
supervised learning of universal sentence representations from natural language inference data,https://github.com/jvdbogae/artverc,N
supervised learning of universal sentence representations from natural language inference data,https://github.com/rockandroll123/natural-language-inference,N
supervised learning of universal sentence representations from natural language inference data,https://github.com/wbglaeser/brexit-sentiment,N
supervised learning of universal sentence representations from natural language inference data,https://github.com/menajosep/AleatoricSent,N
supervised learning of universal sentence representations from natural language inference data,https://github.com/jean-kunz/ml_research_papers,N
supervised learning of universal sentence representations from natural language inference data,https://github.com/avinassh/kylo,N
supervised learning of universal sentence representations from natural language inference data,https://github.com/boknilev/nmt-repr-analysis,N
multi-task deep neural networks for natural language understanding,https://github.com/namisan/mt-dnn,Y
multi-task deep neural networks for natural language understanding,https://github.com/tianxin1860/Papers,N
multi-task deep neural networks for natural language understanding,https://github.com/gaohuan2015/NLPTool,Y
multi-task deep neural networks for natural language understanding,https://github.com/xycforgithub/MultiTask-MRC,Y
glue: a multi-task benchmark and analysis platform for natural language understanding,https://github.com/nyu-mll/GLUE-baselines,N
glue: a multi-task benchmark and analysis platform for natural language understanding,https://github.com/jsalt18-sentence-repl/jiant,N
learning general purpose distributed sentence representations via large scale multi-task learning,https://github.com/Maluuba/gensen,N
learning general purpose distributed sentence representations via large scale multi-task learning,https://github.com/facebookresearch/InferSent,N
learning general purpose distributed sentence representations via large scale multi-task learning,https://github.com/facebookresearch/SentEval,N
natural language inference with hierarchical bilstm max pooling architecture,https://github.com/Helsinki-NLP/HBMP,N
grounded textual entailment,https://github.com/claudiogreco/coling18-gte,Y
improving language understanding by generative pre-training,https://github.com/openai/finetune-transformer-lm,N
multiway attention networks for modeling sentence pairs,https://github.com/zsweet/zsw_AI_model,N
natural language inference over interaction space,https://github.com/YichenGong/Densely-Interactive-Inference-Network,Y
natural language inference over interaction space,https://github.com/YerevaNN/DIIN-in-Keras,Y
bilateral multi-perspective matching for natural language sentences,https://github.com/kunj17/keras-quora-question-pair,Y
bilateral multi-perspective matching for natural language sentences,https://github.com/gprateek-iitk/Quora-question-pair-matching,Y
bilateral multi-perspective matching for natural language sentences,https://github.com/Elvirasun28/quora-question-duplicate,Y
bilateral multi-perspective matching for natural language sentences,https://github.com/vaibhav4595/BiMPM_PyTorch,Y
bilateral multi-perspective matching for natural language sentences,https://github.com/bradleypallen/keras-quora-question-pairs,N
enhanced lstm for natural language inference,https://github.com/blcunlp/CNLI,Y
enhanced lstm for natural language inference,https://github.com/coetaur0/ESIM,Y
enhanced lstm for natural language inference,https://github.com/SJHBXShub/Question_pair,Y
enhanced lstm for natural language inference,https://github.com/rockandroll123/natural-language-inference,Y
enhanced lstm for natural language inference,https://github.com/nyu-mll/multiNLI,N
stochastic answer networks for natural language inference,https://github.com/kevinduh/san_mrc,Y
stochastic answer networks for natural language inference,https://github.com/ks-korovina/my_san,Y
stochastic answer networks for natural language inference,https://github.com/Srinivas-R/Squad,N
stochastic answer networks for natural language inference,https://github.com/Xaniar87/SAN_SQuAD2,Y
a decomposable attention model for natural language inference,https://github.com/blcunlp/CNLI,Y
a decomposable attention model for natural language inference,https://github.com/harvardnlp/struct-attn,Y
a decomposable attention model for natural language inference,https://github.com/libowen2121/SNLI-decomposable-attention,Y
a decomposable attention model for natural language inference,https://github.com/DeNeutoy/Decomposable_Attn,Y
a decomposable attention model for natural language inference,https://github.com/sminer3/duplicate_statement_detection,Y
a decomposable attention model for natural language inference,https://github.com/dmlc/gluon-nlp,N
dynamic meta-embeddings for improved sentence representations,https://github.com/kushalchauhan98/dynamic-meta-embeddings,Y
enhancing sentence embedding with generalized pooling,https://github.com/lukecq1231/generalized-pooling,Y
long short-term memory-networks for machine reading,https://github.com/JRC1995/Abstractive-Summarization,N
long short-term memory-networks for machine reading,https://github.com/xbtlin/All-about-Machine-Learning,Y
learning natural language inference with lstm,https://github.com/fuhuamosi/MatchLstm,Y
learning natural language inference with lstm,https://github.com/shuohangwang/SeqMatchSeq,Y
learning natural language inference with lstm,https://github.com/donghyeonk/match-lstm,Y
learning natural language inference with lstm,https://github.com/donghyeonk/matchlstm,N
learning natural language inference with lstm,https://github.com/junfenglx/reasoning_attention,Y
learning to compose task-specific tree structures,https://github.com/jihunchoi/unsupervised-treelstm,Y
shortcut-stacked sentence encoders for multi-domain inference,https://github.com/KhenAharon/Deep-Learning-SNLI-Residual-Stacked-Encoders,Y
disan: directional self-attention network for rnn/cnn-free language understanding,https://github.com/taoshen58/DiSAN,N
recurrent neural network-based sentence encoder with gated attention for natural language inference,https://github.com/eilon47/DL_Ass4,N
learning natural language inference using bidirectional lstm model and inner-attention,https://github.com/Smerity/keras_snli,N
reasoning about entailment with neural attention,https://github.com/junfenglx/reasoning_attention,Y
reasoning about entailment with neural attention,https://github.com/elkhand/QuoraDuplicates,N
a fast unified model for parsing and sentence understanding,https://github.com/NYU-MLL/spinn,Y
a fast unified model for parsing and sentence understanding,https://github.com/richa912/THANOS,Y
a fast unified model for parsing and sentence understanding,https://github.com/stanfordnlp/spinn,Y
order-embeddings of images and language,https://github.com/ivendrov/order-embedding,Y
data-to-text generation with content selection and planning,https://github.com/ratishsp/data2text-plan-py,N
data-to-text generation with content selection and planning,https://github.com/jugalw13/Red-Hat-Hack,N
challenges in data-to-document generation,https://github.com/harvardnlp/data2text,N
pragmatically informative text generation,https://github.com/sIncerass/prag_generation,N
findings of the e2e nlg challenge,https://github.com/UFAL-DSG/tgen,N
e2e nlg challenge: neural models vs. templates,https://github.com/UKPLab/e2e-nlg-challenge-2017,N
deep graph convolutional encoders for structured data to text generation,https://github.com/diegma/graph-2-text,N
table-to-text generation by structure-aware seq2seq learning,https://github.com/tyliupku/wiki2bio,Y
neural text generation from structured data with application to the biography domain,https://github.com/tyliupku/wiki2bio,N
long text generation via adversarial training with leaked information,https://github.com/CR-Gjx/LeakGAN,Y
long text generation via adversarial training with leaked information,https://github.com/valko073/LyricsGANs,Y
long text generation via adversarial training with leaked information,https://github.com/rupes438/CodeGen,Y
seqgan: sequence generative adversarial nets with policy gradient,https://github.com/suhoy901/SeqGAN,Y
seqgan: sequence generative adversarial nets with policy gradient,https://github.com/AWLyrics/SeqGAN_Poem,Y
seqgan: sequence generative adversarial nets with policy gradient,https://github.com/akashkm99/RL-Paper-Notes,Y
seqgan: sequence generative adversarial nets with policy gradient,https://github.com/L0SG/seqgan-music,N
seqgan: sequence generative adversarial nets with policy gradient,https://github.com/bgenchel/MusicalSeqGAN,Y
seqgan: sequence generative adversarial nets with policy gradient,https://github.com/chung771026/Implement-seqGAN-with-Keras,Y
seqgan: sequence generative adversarial nets with policy gradient,https://github.com/TalkToTheGAN/REGAN,Y
seqgan: sequence generative adversarial nets with policy gradient,https://github.com/vedantc6/SeqGAN,Y
seqgan: sequence generative adversarial nets with policy gradient,https://github.com/rupes438/CodeGen,Y
seqgan: sequence generative adversarial nets with policy gradient,https://github.com/bgenchel/Reinforcement-Learning-for-Music-Generation,N
seqgan: sequence generative adversarial nets with policy gradient,https://github.com/GuyTevet/SeqGAN-eval,Y
seqgan: sequence generative adversarial nets with policy gradient,https://github.com/LantaoYu/SeqGAN,Y
seqgan: sequence generative adversarial nets with policy gradient,https://github.com/Bayesian-Razor/papernotes,Y
lagging inference networks and posterior collapse in variational autoencoders,https://github.com/jxhe/vae-lagging-encoder,Y
semi-amortized variational autoencoders,https://github.com/harvardnlp/sa-vae,Y
improved variational autoencoders for text modeling using dilated convolutions,https://github.com/harvardnlp/sa-vae,N
improved variational autoencoders for text modeling using dilated convolutions,https://github.com/ryokamoi/dcnn_textvae,N
an auto-encoder matching model for learning utterance-level semantic dependency in dialogue generation,https://github.com/lancopku/AMM,N
generating text through adversarial training using skip-thought vectors,https://github.com/enigmaeth/skip-thought-gan,Y
cesi: canonicalizing open knowledge bases using embeddings and side information,https://github.com/malllabiisc/cesi,N
cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting,https://github.com/svishwa/crowdcount-cascaded-mtl,N
swag: a large-scale adversarial dataset for grounded commonsense inference,https://github.com/chiahsuan156/Question-Answering_Resources_Papers,N
a simple method for commonsense reasoning,https://github.com/tensorflow/models/tree/master/research/lm_commonsense,Y
improving the coverage and the generalization ability of neural word sense disambiguation through hypernymy and hyponymy relationships,https://github.com/getalp/disambiguate,N
incorporating glosses into neural word sense disambiguation,https://github.com/jimiyulu/WSD_MemNN,Y
trellis networks for sequence modeling,https://github.com/locuslab/trellisnet,Y
an analysis of neural language modeling at multiple scales,https://github.com/salesforce/awd-lstm-lm,Y
an analysis of neural language modeling at multiple scales,https://github.com/Han-JD/GRU-D,Y
an analysis of neural language modeling at multiple scales,https://github.com/arvieFrydenlund/awd-lstm-lm,Y
an analysis of neural language modeling at multiple scales,https://github.com/ari-holtzman/genlm,Y
neural architecture search with reinforcement learning,https://github.com/barisozmen/deepaugment,Y
neural architecture search with reinforcement learning,https://github.com/cshannonn/blackscholes_nas,Y
direct output connection for a high-rank language model,https://github.com/nttcslab-nlp/doc_lm,Y
breaking the softmax bottleneck: a high-rank rnn language model,https://github.com/nunezpaul/MNIST,Y
breaking the softmax bottleneck: a high-rank rnn language model,https://github.com/zihangdai/mos,Y
breaking the softmax bottleneck: a high-rank rnn language model,https://github.com/cstorm125/thai2fit,Y
breaking the softmax bottleneck: a high-rank rnn language model,https://github.com/tdmeeste/SparseSeqModels,Y
dynamic evaluation of neural sequence models,https://github.com/benkrause/dynamic-evaluation,Y
dynamic evaluation of neural sequence models,https://github.com/sacmehta/PRU,Y
dynamic evaluation of neural sequence models,https://github.com/benkrause/dynamiceval-transformer,Y
partially shuffling the training data to improve language models,https://github.com/ofirpress/PartialShuffle,Y
regularizing and optimizing lstm language models,https://github.com/cstorm125/thai2fit,Y
regularizing and optimizing lstm language models,https://github.com/iwangjian/ByteCup2018,Y
regularizing and optimizing lstm language models,https://github.com/arvieFrydenlund/awd-lstm-lm,Y
regularizing and optimizing lstm language models,https://github.com/Asteur/RERITES-AvgWeightDescentLSTM-PoetryGeneration,Y
regularizing and optimizing lstm language models,https://github.com/alexandra-chron/wassa-2018,Y
regularizing and optimizing lstm language models,https://github.com/jhave/RERITES-AvgWeightDescentLSTM-PoetryGeneration,Y
regularizing and optimizing lstm language models,https://github.com/castorini/Castor,N
regularizing and optimizing lstm language models,https://github.com/Han-JD/GRU-D,Y
regularizing and optimizing lstm language models,https://github.com/tdmeeste/SparseSeqModels,Y
regularizing and optimizing lstm language models,https://github.com/AtheMathmo/AggMo,Y
regularizing and optimizing lstm language models,https://github.com/JessikaSmith/language_model,Y
regularizing and optimizing lstm language models,https://github.com/mamamot/Russian-ULMFit,Y
regularizing and optimizing lstm language models,https://github.com/BenjiKCF/AWD-LSTM-sentiment-classifier,Y
regularizing and optimizing lstm language models,https://github.com/dmlc/gluon-nlp,N
regularizing and optimizing lstm language models,https://github.com/uclanlp/NamedEntityLanguageModel,Y
regularizing and optimizing lstm language models,https://github.com/salesforce/awd-lstm-lm,Y
regularizing and optimizing lstm language models,https://github.com/ari-holtzman/genlm,Y
regularizing and optimizing lstm language models,https://github.com/alexandra-chron/ntua-slp-wassa-iest2018,Y
regularizing and optimizing lstm language models,https://github.com/vganesh46/awd-lstm-pytorch-implementation,Y
regularizing and optimizing lstm language models,https://github.com/prajjwal1/language-modelling,N
transformer-xl: attentive language models beyond a fixed-length context,https://github.com/huggingface/pytorch-pretrained-BERT,Y
transformer-xl: attentive language models beyond a fixed-length context,https://github.com/kimiyoung/transformer-xl,Y
transformer-xl: attentive language models beyond a fixed-length context,https://github.com/benkrause/dynamiceval-transformer,Y
darts: differentiable architecture search,https://github.com/SivanDoveh/AGAN,N
darts: differentiable architecture search,https://github.com/victoriaqiu/10707finalproject,Y
darts: differentiable architecture search,https://github.com/snownus/COOP,Y
darts: differentiable architecture search,https://github.com/wxl002/darts,Y
darts: differentiable architecture search,https://github.com/quark0/darts,Y
darts: differentiable architecture search,https://github.com/liamcli/darts,Y
darts: differentiable architecture search,https://github.com/seriousssam/deep-image-prior-transfer,Y
darts: differentiable architecture search,https://github.com/CiscoAI/amla,Y
darts: differentiable architecture search,https://github.com/khanrc/pt.darts,Y
darts: differentiable architecture search,https://github.com/osmr/imgclsmob,Y
darts: differentiable architecture search,https://github.com/HeungChangLee/AutoML,Y
darts: differentiable architecture search,https://github.com/dragen1860/DARTS-PyTorch,Y
darts: differentiable architecture search,https://github.com/tinhb92/rnn_darts_fastai,N
darts: differentiable architecture search,https://github.com/JunrQ/NAS,Y
darts: differentiable architecture search,https://github.com/adammenges/DARTS-PyTorch,Y
fraternal dropout,https://github.com/kondiz/fraternal-dropout,Y
efficient neural architecture search via parameter sharing,https://github.com/MINGUKKANG/PNU_Termproject_ENAS,Y
efficient neural architecture search via parameter sharing,https://github.com/melodyguan/enas,Y
efficient neural architecture search via parameter sharing,https://github.com/aymanshams07/enas_cifar10,Y
efficient neural architecture search via parameter sharing,https://github.com/countif/enas_nni,Y
efficient neural architecture search via parameter sharing,https://github.com/MengTianjian/enas-pytorch,Y
efficient neural architecture search via parameter sharing,https://github.com/ahundt/renas,Y
efficient neural architecture search via parameter sharing,https://github.com/zbyte64/pytorch-dagsearch,Y
efficient neural architecture search via parameter sharing,https://github.com/MINGUKKANG/PNU_Capstone_Design,Y
efficient neural architecture search via parameter sharing,https://github.com/skyhawk1990/reference,Y
efficient neural architecture search via parameter sharing,https://github.com/Ezereal/enas,Y
efficient neural architecture search via parameter sharing,https://github.com/CiscoAI/amla,Y
efficient neural architecture search via parameter sharing,https://github.com/MINGUKKANG/ENAS-Tensorflow,Y
efficient neural architecture search via parameter sharing,https://github.com/cshannonn/blackscholes_nas,Y
efficient neural architecture search via parameter sharing,https://github.com/ahundt/enas,Y
efficient neural architecture search via parameter sharing,https://github.com/kaileymonn/Quantized-ENAS-ConvNets,Y
recurrent highway networks,https://github.com/julian121266/RecurrentHighwayNetworks,Y
tying word vectors and word classifiers: a loss framework for language modeling,https://github.com/JianGoForIt/YellowFin_Pytorch,N
tying word vectors and word classifiers: a loss framework for language modeling,https://github.com/Ravoxsg/Word-level-language-modeling,N
tying word vectors and word classifiers: a loss framework for language modeling,https://github.com/rdspring1/PyTorch_GBW_LM,N
tying word vectors and word classifiers: a loss framework for language modeling,https://github.com/floydhub/word-language-model,N
dynamic evaluation of transformer language models,https://github.com/benkrause/dynamiceval-transformer,N
hierarchical multiscale recurrent neural networks,https://github.com/kaiu85/hm-rnn,Y
adaptive input representations for neural language modeling,https://github.com/pytorch/fairseq,N
adaptive input representations for neural language modeling,https://github.com/AranKomat/adapinp,Y
language modeling with gated convolutional networks,https://github.com/pranav-ust/nlm,Y
language modeling with gated convolutional networks,https://github.com/ibatra/nlm,Y
language modeling with gated convolutional networks,https://github.com/stikbuf/Language_Modeling,Y
language modeling with gated convolutional networks,https://github.com/facebookresearch/fairseq-py,N
improving neural language models with a continuous cache,https://github.com/arvieFrydenlund/awd-lstm-lm,Y
improving neural language models with a continuous cache,https://github.com/Asteur/RERITES-AvgWeightDescentLSTM-PoetryGeneration,Y
improving neural language models with a continuous cache,https://github.com/jhave/RERITES-AvgWeightDescentLSTM-PoetryGeneration,Y
improving neural language models with a continuous cache,https://github.com/uclanlp/NamedEntityLanguageModel,Y
improving neural language models with a continuous cache,https://github.com/dmlc/gluon-nlp,N
improving neural language models with a continuous cache,https://github.com/salesforce/awd-lstm-lm,Y
improving neural language models with a continuous cache,https://github.com/ari-holtzman/genlm,Y
mesh-tensorflow: deep learning for supercomputers,https://github.com/tensorflow/mesh,Y
exploring the limits of language modeling,https://github.com/UnofficialJuliaMirror/DeepMark-deepmark,Y
exploring the limits of language modeling,https://github.com/DeepMark/deepmark,Y
exploring the limits of language modeling,https://github.com/DhruvDh/semisupervised_nlu,Y
exploring the limits of language modeling,https://github.com/rafaljozefowicz/lm,Y
exploring the limits of language modeling,https://github.com/IBM/MAX-News-Text-Generator,Y
exploring the limits of language modeling,https://github.com/UnofficialJuliaMirrorSnapshots/DeepMark-deepmark,Y
exploring the limits of language modeling,https://github.com/tensorflow/models/tree/master/research/lm_1b,Y
exploring the limits of language modeling,https://github.com/rdspring1/PyTorch_GBW_LM,Y
exploring the limits of language modeling,https://github.com/dmlc/gluon-nlp,N
exploring the limits of language modeling,https://github.com/okuchaiev/f-lm,Y
factorization tricks for lstm networks,https://github.com/okuchaiev/f-lm,N
factorization tricks for lstm networks,https://github.com/rdspring1/PyTorch_GBW_LM,Y
one billion word benchmark for measuring progress in statistical language modeling,https://github.com/tensorflow/models/tree/master/research/lm_1b,N
one billion word benchmark for measuring progress in statistical language modeling,https://github.com/IBM/MAX-News-Text-Generator,N
recurrent batch normalization,https://github.com/cooijmanstim/recurrent-batch-normalization,Y
learning graph-level representation for drug discovery,https://github.com/microljy/graph_level_drug_discovery,Y
learning graph-level representation for drug discovery,https://github.com/ZJULearning/graph_level_drug_discovery,Y
convolutional networks on graphs for learning molecular fingerprints,https://github.com/SystemicCypher/Neural-Molecule-Fingerprints,Y
convolutional networks on graphs for learning molecular fingerprints,https://github.com/Sarikaya-Lab-GEMSEC/Peptide_Graph_Autograd,Y
convolutional networks on graphs for learning molecular fingerprints,https://github.com/debbiemarkslab/neural-fingerprint-theano,Y
convolutional networks on graphs for learning molecular fingerprints,https://github.com/onakanob/Peptide_Graph_Autograd,Y
convolutional networks on graphs for learning molecular fingerprints,https://github.com/HIPS/neural-fingerprint,Y
neural message passing for quantum chemistry,https://github.com/priba/nmp_qc,Y
neural message passing for quantum chemistry,https://github.com/CoderPat/OpenGNN,Y
neural message passing for quantum chemistry,https://github.com/gajeelSF/Reinforcement-Learning-Branch-and-Bound,Y
neural message passing for quantum chemistry,https://github.com/Microsoft/gated-graph-neural-network-samples,Y
neural message passing for quantum chemistry,https://github.com/cts198859/deeprl_dist,Y
gated graph sequence neural networks,https://github.com/yujiali/ggnn,Y
gated graph sequence neural networks,https://github.com/Microsoft/gated-graph-neural-network-samples,Y
gated graph sequence neural networks,https://github.com/bdqnghi/bi-tbcnn,Y
gated graph sequence neural networks,https://github.com/entslscheia/GGNN_Reasoning,Y
self-normalizing neural networks,https://github.com/jonnor/datascience-master,N
self-normalizing neural networks,https://github.com/BenjiKCF/SNN-with-embeddings-for-Malware-Prediction,Y
self-normalizing neural networks,https://github.com/domschl/syncognite,Y
self-normalizing neural networks,https://github.com/bioinf-jku/SNNs,Y
self-normalizing neural networks,https://github.com/BioImageInformatics/tfmodels,Y
self-normalizing neural networks,https://github.com/TakuyaShintate/node,Y
self-normalizing neural networks,https://github.com/blackPacha/VAE_ABIDE1,Y
automatic skin lesion segmentation with fully convolutional-deconvolutional networks,https://github.com/manideep2510/melanoma_segmentation,N
a novel focal tversky loss function with improved attention u-net for lesion segmentation,https://github.com/nabsabraham/focal-tversky-unet,N
brain tumor segmentation with deep neural networks,https://github.com/IAmSuyogJadhav/Brainy,Y
brain tumor segmentation with deep neural networks,https://github.com/naldeborgh7575/brain_segmentation,N
brain tumor segmentation with deep neural networks,https://github.com/tobinthankachan1/exam1,N
brain tumor segmentation with deep neural networks,https://github.com/peteraugustine/seg3,N
brain tumor segmentation with deep neural networks,https://github.com/ajinas-ibrahim/brain_tumor,N
brain tumor segmentation with deep neural networks,https://github.com/jadevaibhav/Brain-Tumor-Segmentation-using-Deep-Neural-networks,N
brain tumor segmentation with deep neural networks,https://github.com/dijju/mri-cnn,N
brain tumor segmentation and radiomics survival prediction: contribution to the brats 2017 challenge,https://github.com/Jack-Etheredge/Brain-Tumor-Segmentation-3D-UNet-CNN,N
autofocus layer for semantic segmentation,https://github.com/perslev/Autofocus-Layer-TF,N
autofocus layer for semantic segmentation,https://github.com/yaq007/Autofocus-Layer,Y
efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation,https://github.com/yaq007/Autofocus-Layer,N
3d mri brain tumor segmentation using autoencoder regularization,https://github.com/IAmSuyogJadhav/3d-mri-brain-tumor-segmentation-using-autoencoder-regularization,N
recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation,https://github.com/TheInfamousWayne/UNet,N
recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation,https://github.com/LeeJunHyun/Image_Segmentation,N
road extraction by deep residual u-net,https://github.com/Lkruitwagen/remote_sensing_solar_pv,N
road extraction by deep residual u-net,https://github.com/Kaido0/Brain-Tissue-Segment-Keras,Y
road extraction by deep residual u-net,https://github.com/nikhilroxtomar/Deep-Residual-Unet,N
u-net: convolutional networks for biomedical image segmentation,https://github.com/pydsgz/DeepVOG,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/chinakook/U-Net,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/minerva-ml/open-solution-mapping-challenge,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/ternaus/angiodysplasia-segmentation,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/alexklibisz/deep-calcium,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/kaland313/vitmav45-ShipSeakers,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/jkhadley/capstone-project,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/TheInfamousWayne/UNet,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/clarencenhuang/dl-colorize,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/Jack-Etheredge/Brain-Tumor-Segmentation-3D-UNet-CNN,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/Muthu2093/U-Net-for-Brain-Tumor-Segmentation,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/goyalyash1224/Nuclei-detection-and-segmentaion,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/overshiki/unet-pytorch,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/hycis/TensorGraph,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/orobix/retina-unet,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/perslev/MultiPlanarUNet,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/yumaloop/kaggle-tgs-salt-competition,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/petko-nikolov/pysemseg,N
u-net: convolutional networks for biomedical image segmentation,https://github.com/preritj/segmentation,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/sinandeger/RedeemTheBoar,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/aksub99/U-Net-Pytorch,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/SeonbeomKim/TensorFlow-pix2pix,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/oliverz17/Unet_Keras,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/jvanvugt/pytorch-unet,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/nithi89/unet_darknet,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/raun1/MICCAI2018---Complementary_Segmentation_Network-Raw-Code-Available-Under-Construction-,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/HMS-IDAC/UNet,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/raun1/Complementary_Segmentation_Network-Raw-Code-Available-Under-Construction-,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/sadari1/TumorDetectionDeepLearning,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/PatriciaRodrigues1994/Satellite_Image_Segmentation,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/neuropoly/multiclass-segmentation,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/DarkGeekMS/Semantic_Segmentation_Models_Keras,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/Jeongseungwoo/U_net,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/gmatasci/SemanticSegmentation,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/shermanhung/U-Net,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/XiaowenK/UNet-for-Nodule-Detection-xray,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/HadiZayer/UNet_pytorch,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/ImagingLab/Colorizing-with-GANs,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/Lkruitwagen/remote_sensing_solar_pv,N
u-net: convolutional networks for biomedical image segmentation,https://github.com/kritanu82/healthif.ai,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/TobiasGruening/ARU-Net,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/zhugoldman/CNN-segmentation-for-Lung-cancer-OARs,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/cosmic-cortex/pytorch-UNet,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/LilyHu/image_segmentation_chromosomes,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/yassineAlouini/data-science-bowl-2018,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/Barak123748596/CVDL_course_project,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/muramasa8191/DeepLearning,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/ArkaJU/U-Net-Satellite,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/pricebenjamin/unet-estimator,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/plesqui/4d-cbct,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/YourGc/Unet_jinnan2,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/jtiger958/pytorch-computer-vision-basic,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/VideoUpScaler/Video_Up_Scalers,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/ternaus/TernausNetV2,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/massucattoj/unet-model,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/ubamba98/Brain-Segmentation,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/ankit-ai/GAN_breast_mammography_segmentation,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/ligenchang/carvana-image-masking-challenge-michael,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/andrewowens/multisensory,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/ethanhou99/ml_learn,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/PatriciaRodrigues1994/Satellite-Image-Segmentation,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/mariosfourn/ScienceBowl,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/IAmSuyogJadhav/Brainy,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/jtiger958/pytorch-computer-vision-tutorial,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/shenshutao/image_segmentation,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/ZFTurbo/ZF_UNET_224_Pretrained_Model,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/vlievin/Unet,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/LeeJunHyun/Image_Segmentation,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/koriavinash1/Fetal-Brain-Segmentation,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/wlstyql/keras_with_mat_for_unet,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/Hayden2018/Road-Segmentation,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/melissande/dhi-segmentation-buildings,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/raun1/MICCAI2018---Complementary_Segmentation_Network-Raw-Code,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/udacity/MLND-CN-Capstone-TGSImage,N
u-net: convolutional networks for biomedical image segmentation,https://github.com/KevinHexin/unet_tensorflow,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/kilgore92/Probabalistic-U-Net,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/jiandai/mlTst,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/resonance20/segmenter,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/JHLee0513/Salt_detection_challenge,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/AICIP-UTK/spacenet-round3,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/jellevankerk/Team-Challenge,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/tsixta/jnet,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/nicolajbky/100daysofML,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/a-martyn/unet,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/ugent-korea/pytorch-unet-segmentation,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/Shumway82/U-Net-Segmentation,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/EdwardTyantov/ultrasound-nerve-segmentation,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/shirans/cv_course_project,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/K-Mike/Automatic-salt-deposits-segmentation,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/biss/unet,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/FelixGruen/tensorflow-u-net,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/jovenwayfarer/pneumonia,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/sananand007/StrangerThings,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/ruixif/Unet_keras,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/YudeWang/UNet-Satellite-Image-Segmentation,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/tanyanair/segmentation_uncertainty,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/sraashis/ature,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/gvtulder/elasticdeform,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/JosephPB/XNet,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/SixQuant/U-Net,Y
u-net: convolutional networks for biomedical image segmentation,https://github.com/neshitov/UNet,Y
an application of cascaded 3d fully convolutional networks for medical image segmentation,https://github.com/holgerroth/3Dunet_abdomen_cascade,N
attention u-net: learning where to look for the pancreas,https://github.com/aparecidovieira/Road_extraction,N
attention u-net: learning where to look for the pancreas,https://github.com/ktaebum/AttentionedDeepPaint,N
attention u-net: learning where to look for the pancreas,https://github.com/nabsabraham/focal-tversky-unet,N
attention u-net: learning where to look for the pancreas,https://github.com/ozan-oktay/Attention-Gated-Networks,N
attention u-net: learning where to look for the pancreas,https://github.com/TheInfamousWayne/UNet,Y
attention u-net: learning where to look for the pancreas,https://github.com/LeeJunHyun/Image_Segmentation,Y
large scale image segmentation with structured loss based deep learning for connectome reconstruction,https://github.com/funkey/mala,N
large scale image segmentation with structured loss based deep learning for connectome reconstruction,https://github.com/funkey/waterz,N
deep cnn ensembles and suggestive annotations for infant brain mri segmentation,https://github.com/josedolz/SemiDenseNet,N
v-net: fully convolutional neural networks for volumetric medical image segmentation,https://github.com/pydsgz/DeepVOG,N
v-net: fully convolutional neural networks for volumetric medical image segmentation,https://github.com/mattmacy/vnet.pytorch,N
v-net: fully convolutional neural networks for volumetric medical image segmentation,https://github.com/assassint2017/MICCAI-LITS2017,N
v-net: fully convolutional neural networks for volumetric medical image segmentation,https://github.com/MiguelMonteiro/VNet-Tensorflow,N
conditional random fields as recurrent neural networks for 3d medical imaging segmentation,https://github.com/MiguelMonteiro/CRFasRNNLayer,N
hyperdense-net: a hyper-densely connected cnn for multi-modal image segmentation,https://github.com/josedolz/HyperDenseNet,N
implicit quantile networks for distributional reinforcement learning,https://github.com/pihey1995/DistributionalRL,Y
implicit quantile networks for distributional reinforcement learning,https://github.com/sjYoondeltar/myRL_example,Y
implicit quantile networks for distributional reinforcement learning,https://github.com/google/dopamine,Y
implicit quantile networks for distributional reinforcement learning,https://github.com/xiaosonggege/dopamine-master,N
implicit quantile networks for distributional reinforcement learning,https://github.com/sjYoondeltar/IQN_example,Y
implicit quantile networks for distributional reinforcement learning,https://github.com/KatyNTsachi/Hierarchical-RL,Y
implicit quantile networks for distributional reinforcement learning,https://github.com/PierreAlexSW/distributional_rl,Y
exploration by random network distillation,https://github.com/DuaneNielsen/rnd,Y
exploration by random network distillation,https://github.com/lgerrets/rl18-curiosity,Y
distributed prioritized experience replay,https://github.com/eladsar/rbi,Y
distributed prioritized experience replay,https://github.com/dannysdeng/dqn-pytorch,Y
distributed prioritized experience replay,https://github.com/haje01/distper,Y
distributed prioritized experience replay,https://github.com/sherry4186/Distributed-DQN,Y
distributed prioritized experience replay,https://github.com/mightypirate1/DRL-Tetris,Y
impala: scalable distributed deep-rl with importance weighted actor-learner architectures,https://github.com/haje01/impala,N
impala: scalable distributed deep-rl with importance weighted actor-learner architectures,https://github.com/crazydonkey200/neural-symbolic-machines,N
impala: scalable distributed deep-rl with importance weighted actor-learner architectures,https://github.com/windstrip/DeepMind-StreetLearn,N
impala: scalable distributed deep-rl with importance weighted actor-learner architectures,https://github.com/deepmind/streetlearn,N
impala: scalable distributed deep-rl with importance weighted actor-learner architectures,https://github.com/deepmind/scalable_agent,Y
impala: scalable distributed deep-rl with importance weighted actor-learner architectures,https://github.com/theSparta/neural-symbolic-machines,N
dueling network architectures for deep reinforcement learning,https://github.com/JuliaPOMDP/DeepQLearning.jl,Y
dueling network architectures for deep reinforcement learning,https://github.com/chagmgang/OnlyDQNRL,N
dueling network architectures for deep reinforcement learning,https://github.com/Adrelf/DRL-navigation,Y
dueling network architectures for deep reinforcement learning,https://github.com/wtingda/DeepRLBreakout,Y
dueling network architectures for deep reinforcement learning,https://github.com/kshitij-ingale/Reinforcement-Learning,Y
dueling network architectures for deep reinforcement learning,https://github.com/nathanin/pad,Y
dueling network architectures for deep reinforcement learning,https://github.com/facebookresearch/Horizon,Y
dueling network architectures for deep reinforcement learning,https://github.com/Brandon-Rozek/DeepRL,Y
dueling network architectures for deep reinforcement learning,https://github.com/opplieam/Pong-Deep-RL,Y
dueling network architectures for deep reinforcement learning,https://github.com/jsztompka/DuelDQN,Y
dueling network architectures for deep reinforcement learning,https://github.com/NervanaSystems/coach,Y
dueling network architectures for deep reinforcement learning,https://github.com/utarumo/RL_implementation,Y
dueling network architectures for deep reinforcement learning,https://github.com/zynk13/dueling-dqn-Reinforcement-learning,Y
dueling network architectures for deep reinforcement learning,https://github.com/R-Sweke/DeepQ-Decoding,Y
dueling network architectures for deep reinforcement learning,https://github.com/MEOWMEOW114/nd893-p1-navigation-banana,Y
dueling network architectures for deep reinforcement learning,https://github.com/prajwalgatti/DRL-Continuous-Control,Y
dueling network architectures for deep reinforcement learning,https://github.com/prajwalgatti/DRL-Navigation,Y
dueling network architectures for deep reinforcement learning,https://github.com/hmhyau/Atari_DDDQN,Y
dueling network architectures for deep reinforcement learning,https://github.com/jezzarax/drlnd_p1_navigation,Y
dueling network architectures for deep reinforcement learning,https://github.com/FaboNo/DRLND,Y
dueling network architectures for deep reinforcement learning,https://github.com/austinsilveria/Banana-Collection-DQN,Y
dueling network architectures for deep reinforcement learning,https://github.com/170928/-Review-Dueling-Deep-Q-Network,Y
prioritized experience replay,https://github.com/JuliaPOMDP/DeepQLearning.jl,Y
prioritized experience replay,https://github.com/chagmgang/OnlyDQNRL,N
prioritized experience replay,https://github.com/utarumo/RL_implementation,Y
prioritized experience replay,https://github.com/ameet-1997/Prioritized_Experience_Replay,Y
prioritized experience replay,https://github.com/snhwang/p2-continuous-control-SNH,Y
prioritized experience replay,https://github.com/google/dopamine,Y
prioritized experience replay,https://github.com/snhwang/p3_collab-compet,Y
prioritized experience replay,https://github.com/Adrelf/DRL-navigation,Y
prioritized experience replay,https://github.com/xiaosonggege/dopamine-master,N
prioritized experience replay,https://github.com/Brandon-Rozek/DeepRL,Y
prioritized experience replay,https://github.com/snhwang/p1_navigation_SNH,Y
prioritized experience replay,https://github.com/kayuksel/pytorch-ars,Y
prioritized experience replay,https://github.com/SimonRamstedt/ddpg,Y
prioritized experience replay,https://github.com/NervanaSystems/coach,Y
prioritized experience replay,https://github.com/austinsilveria/Banana-Collection-DQN,Y
prioritized experience replay,https://github.com/Damcy/prioritized-experience-replay,Y
prioritized experience replay,https://github.com/MEOWMEOW114/nd893-p1-navigation-banana,Y
prioritized experience replay,https://github.com/KatyNTsachi/Hierarchical-RL,Y
prioritized experience replay,https://github.com/dtak/hip-mdp-public,Y
prioritized experience replay,https://github.com/Sharda-Borse/ReinforcementLearning,Y
deep exploration via bootstrapped dqn,https://github.com/NervanaSystems/coach,Y
deep exploration via bootstrapped dqn,https://github.com/taimoorchatoor/Bootstrapped_DQN,Y
deep exploration via bootstrapped dqn,https://github.com/tensorflow/models/tree/master/research/deep_contextual_bandits,Y
deep exploration via bootstrapped dqn,https://github.com/mrahtz/learning-from-human-preferences,Y
asynchronous methods for deep reinforcement learning,https://github.com/muupan/async-rl,Y
asynchronous methods for deep reinforcement learning,https://github.com/wxj77/TransferReinforcementLearning,Y
asynchronous methods for deep reinforcement learning,https://github.com/miyosuda/async_deep_reinforce,Y
asynchronous methods for deep reinforcement learning,https://github.com/wtingda/DeepRLBreakout,Y
asynchronous methods for deep reinforcement learning,https://github.com/avillemin/Minecraft-AI,N
asynchronous methods for deep reinforcement learning,https://github.com/ikostrikov/pytorch-a3c,N
asynchronous methods for deep reinforcement learning,https://github.com/Jventajas/Reinforcement-Learning,N
asynchronous methods for deep reinforcement learning,https://github.com/AI-RG/rl-experiments,Y
asynchronous methods for deep reinforcement learning,https://github.com/tensorpack/tensorpack,N
asynchronous methods for deep reinforcement learning,https://github.com/joshiatul/game_playing,Y
asynchronous methods for deep reinforcement learning,https://github.com/khanhptnk/bandit-nmt,Y
asynchronous methods for deep reinforcement learning,https://github.com/aabbeell/reinforcementLearning.a2c.gym,Y
asynchronous methods for deep reinforcement learning,https://github.com/NervanaSystems/coach,Y
asynchronous methods for deep reinforcement learning,https://github.com/grananqvist/reinforcement-learning-super-mario-A3C,Y
asynchronous methods for deep reinforcement learning,https://github.com/ShibiHe/Q-Optimality-Tightening,N
asynchronous methods for deep reinforcement learning,https://github.com/openai/universe-starter-agent,Y
asynchronous methods for deep reinforcement learning,https://github.com/Jzar/Space-Invaders-DQN,Y
asynchronous methods for deep reinforcement learning,https://github.com/cdesilv1/sc2_ai_cdes,Y
asynchronous methods for deep reinforcement learning,https://github.com/hill-a/stable-baselines,N
asynchronous methods for deep reinforcement learning,https://github.com/AntGro/P3A-Deep-Reinforcement-Learning,Y
asynchronous methods for deep reinforcement learning,https://github.com/4rChon/NL-FuN,Y
asynchronous methods for deep reinforcement learning,https://github.com/Nasdin/ReinforcementLearning-AtariGame,Y
asynchronous methods for deep reinforcement learning,https://github.com/vladfi1/universe-starter-agent,Y
asynchronous methods for deep reinforcement learning,https://github.com/ikostrikov/pytorch-rl,Y
asynchronous methods for deep reinforcement learning,https://github.com/kngwyu/Rainy,Y
deep reinforcement learning with double q-learning,https://github.com/chagmgang/OnlyDQNRL,N
deep reinforcement learning with double q-learning,https://github.com/moduIo/Deep-Q-network,Y
deep reinforcement learning with double q-learning,https://github.com/Adrelf/DRL-navigation,Y
deep reinforcement learning with double q-learning,https://github.com/wtingda/DeepRLBreakout,Y
deep reinforcement learning with double q-learning,https://github.com/jadag/DDQN_mario,Y
deep reinforcement learning with double q-learning,https://github.com/kshitij-ingale/Reinforcement-Learning,Y
deep reinforcement learning with double q-learning,https://github.com/xgfelicia/Reinforcement-Learning,Y
deep reinforcement learning with double q-learning,https://github.com/facebookresearch/Horizon,Y
deep reinforcement learning with double q-learning,https://github.com/JonasRSV/DQN,Y
deep reinforcement learning with double q-learning,https://github.com/ifestus/rl,Y
deep reinforcement learning with double q-learning,https://github.com/JonasRSV/DQNTensorflow,Y
deep reinforcement learning with double q-learning,https://github.com/ssainz/reinforcement_learning_algorithms,Y
deep reinforcement learning with double q-learning,https://github.com/tensorpack/tensorpack,N
deep reinforcement learning with double q-learning,https://github.com/matthewsparr/Deep-Zork,Y
deep reinforcement learning with double q-learning,https://github.com/utarumo/RL_implementation,Y
deep reinforcement learning with double q-learning,https://github.com/mrdeanplumbley/NavigationRL,Y
deep reinforcement learning with double q-learning,https://github.com/wmol4/Pytorch_DDQN_Unity_Navigation,Y
deep reinforcement learning with double q-learning,https://github.com/MEOWMEOW114/nd893-p1-navigation-banana,Y
deep reinforcement learning with double q-learning,https://github.com/NikolausBerl/Udacity_DRLN_Navigation_Project,Y
deep reinforcement learning with double q-learning,https://github.com/hmhyau/Atari_DDDQN,Y
deep reinforcement learning with double q-learning,https://github.com/Codernauti/Exploration-of-DQN-in-CartPole-Environment,Y
deep reinforcement learning with double q-learning,https://github.com/jezzarax/drlnd_p1_navigation,Y
deep reinforcement learning with double q-learning,https://github.com/gznyyb/deep_reinforcement_learning_Pong,Y
deep reinforcement learning with double q-learning,https://github.com/puppetect/TradingBot-tensorflow,Y
deep reinforcement learning with double q-learning,https://github.com/FaboNo/DRLND,Y
deep reinforcement learning with double q-learning,https://github.com/austinsilveria/Banana-Collection-DQN,Y
deep reinforcement learning with double q-learning,https://github.com/molomono/CartPole_Optimized_DDQN,Y
deep reinforcement learning with double q-learning,https://github.com/pathway/alphaxos,Y
a distributional perspective on reinforcement learning,https://github.com/pihey1995/DistributionalRL,Y
a distributional perspective on reinforcement learning,https://github.com/parilo/gym_bipedal_walker_v2_solution,Y
a distributional perspective on reinforcement learning,https://github.com/rhshi/Minitaur,Y
a distributional perspective on reinforcement learning,https://github.com/NervanaSystems/coach,Y
a distributional perspective on reinforcement learning,https://github.com/PierreAlexSW/distributional_rl,Y
evolution strategies as a scalable alternative to reinforcement learning,https://github.com/ShangtongZhang/DistributedES,Y
evolution strategies as a scalable alternative to reinforcement learning,https://github.com/fiberleif/evolution-strategies,Y
evolution strategies as a scalable alternative to reinforcement learning,https://github.com/alisidd/Evolution-Strategies,Y
evolution strategies as a scalable alternative to reinforcement learning,https://github.com/patniemeyer/ga-autoencoder,Y
evolution strategies as a scalable alternative to reinforcement learning,https://github.com/evaboost/evaboost,Y
evolution strategies as a scalable alternative to reinforcement learning,https://github.com/czen88/q-trader,Y
evolution strategies as a scalable alternative to reinforcement learning,https://github.com/FlixCoder/rust-es-optimizer,Y
evolution strategies as a scalable alternative to reinforcement learning,https://github.com/openai/evolution-strategies-starter,Y
evolution strategies as a scalable alternative to reinforcement learning,https://github.com/aspk/bots_open_ai_gym,Y
the arcade learning environment: an evaluation platform for general agents,https://github.com/mgbellemare/Arcade-Learning-Environment,Y
the arcade learning environment: an evaluation platform for general agents,https://github.com/kenjyoung/MinAtar,N
#exploration: a study of count-based exploration for deep reinforcement learning,https://github.com/nhynes/abc,N
incentivizing exploration in reinforcement learning with deep predictive models,https://github.com/CoffeeddCat/Multiagent_chainMDP,N
playing atari with deep reinforcement learning,https://github.com/JuliaPOMDP/DeepQLearning.jl,Y
playing atari with deep reinforcement learning,https://github.com/xkiwilabs/DQN_Unity_Keras,Y
playing atari with deep reinforcement learning,https://github.com/BH4/Deep-Reinforcement-Learning,Y
playing atari with deep reinforcement learning,https://github.com/avillemin/Minecraft-AI,N
playing atari with deep reinforcement learning,https://github.com/kshitij-ingale/Reinforcement-Learning,Y
playing atari with deep reinforcement learning,https://github.com/KatyNTsachi/Hierarchical-RL,N
playing atari with deep reinforcement learning,https://github.com/nathanin/pad,Y
playing atari with deep reinforcement learning,https://github.com/sunjeet95/Deep-Q-Network-using-Tensorflow,Y
playing atari with deep reinforcement learning,https://github.com/JonasRSV/DQN,Y
playing atari with deep reinforcement learning,https://github.com/Linging/Traffic-Signal-Control,Y
playing atari with deep reinforcement learning,https://github.com/JonasRSV/DQNTensorflow,Y
playing atari with deep reinforcement learning,https://github.com/harvitronix/reinforcement-learning-car,Y
playing atari with deep reinforcement learning,https://github.com/spragunr/deep_q_rl,Y
playing atari with deep reinforcement learning,https://github.com/malteprinzler/bomberman_AI,N
playing atari with deep reinforcement learning,https://github.com/tensorpack/tensorpack,N
playing atari with deep reinforcement learning,https://github.com/joshiatul/game_playing,Y
playing atari with deep reinforcement learning,https://github.com/omkarv/pong-from-pixels,Y
playing atari with deep reinforcement learning,https://github.com/KavindaKottege/DeepQ-Pong,N
playing atari with deep reinforcement learning,https://github.com/Gary-Shi/Tank,Y
playing atari with deep reinforcement learning,https://github.com/ecra93/deep-q-network,N
playing atari with deep reinforcement learning,https://github.com/xDavidos/Deep-Q-learning-applied-to-quantum-error-correction,Y
playing atari with deep reinforcement learning,https://github.com/sygi/deep_q_rl,Y
playing atari with deep reinforcement learning,https://github.com/NervanaSystems/coach,N
playing atari with deep reinforcement learning,https://github.com/jonaths/tf-dqn,N
playing atari with deep reinforcement learning,https://github.com/ecra93/reinforcement-learning,N
playing atari with deep reinforcement learning,https://github.com/mrdeanplumbley/NavigationRL,Y
playing atari with deep reinforcement learning,https://github.com/jonaths/dqn-grid,N
playing atari with deep reinforcement learning,https://github.com/pavitrakumar78/Playing-custom-games-using-Deep-Learning,Y
playing atari with deep reinforcement learning,https://github.com/igoracmorais/inteligencia_artificial,Y
playing atari with deep reinforcement learning,https://github.com/mfregeau/DeepLearning,Y
playing atari with deep reinforcement learning,https://github.com/ninja18/AtariDQN,N
playing atari with deep reinforcement learning,https://github.com/joonleesky/DL_Lectures,N
playing atari with deep reinforcement learning,https://github.com/CankayaUniversity/ceng-407-408-License-Plate-Recognition-Using-Deep-Learning,N
playing atari with deep reinforcement learning,https://github.com/hill-a/stable-baselines,N
playing atari with deep reinforcement learning,https://github.com/blakeMilner/DeepQLearning,N
playing atari with deep reinforcement learning,https://github.com/ugo-nama-kun/DQN-chainer,Y
playing atari with deep reinforcement learning,https://github.com/R-Stefano/DQN,Y
playing atari with deep reinforcement learning,https://github.com/alfredvc/paac,Y
playing atari with deep reinforcement learning,https://github.com/tcmxx/CNTKUnityTools,Y
playing atari with deep reinforcement learning,https://github.com/joonleesky/DeepLearing_Lectures,N
playing atari with deep reinforcement learning,https://github.com/realjohnward/CLTrainer,Y
playing atari with deep reinforcement learning,https://github.com/parilo/rl-server,Y
playing atari with deep reinforcement learning,https://github.com/borhanreo/Obstacle-Avoid-Car,Y
count-based exploration with the successor representation,https://github.com/mcmachado/count_based_exploration_sr,Y
wavenet: a generative model for raw audio,https://github.com/philipperemy/keras-tcn,Y
wavenet: a generative model for raw audio,https://github.com/basameera/NIPS_week_4_5,Y
wavenet: a generative model for raw audio,https://github.com/Stephenzwang/lipreader,Y
wavenet: a generative model for raw audio,https://github.com/PhilippeNguyen/keras_wavenet,Y
wavenet: a generative model for raw audio,https://github.com/HaiFengZeng/clari_wavenet_vocoder,Y
wavenet: a generative model for raw audio,https://github.com/kingstarcraft/speech-to-text-wavenet2,Y
wavenet: a generative model for raw audio,https://github.com/bckpkol/old_game_voice_upsampler,Y
wavenet: a generative model for raw audio,https://github.com/buriburisuri/speech-to-text-wavenet,N
wavenet: a generative model for raw audio,https://github.com/ShichengChen/WaveNetSeparateAudio,Y
wavenet: a generative model for raw audio,https://github.com/Baichenjia/Tensorflow-TCN,Y
wavenet: a generative model for raw audio,https://github.com/NVIDIA/nv-wavenet,Y
wavenet: a generative model for raw audio,https://github.com/TanUkkii007/wavenet,Y
wavenet: a generative model for raw audio,https://github.com/freedombenLiu/speech-to-text-wavenet,Y
wavenet: a generative model for raw audio,https://github.com/JoshuaLeland/WaveNetContinuous,Y
wavenet: a generative model for raw audio,https://github.com/liguigui/speech-to-text-wavenet,Y
wavenet: a generative model for raw audio,https://github.com/adityaagrawal7/speech-to-text-wavenet,Y
wavenet: a generative model for raw audio,https://github.com/scpark20/universal-music-translation,Y
wavenet: a generative model for raw audio,https://github.com/benmoseley/simple-wavenet,Y
wavenet: a generative model for raw audio,https://github.com/r9y9/wavenet,Y
wavenet: a generative model for raw audio,https://github.com/basveeling/wavenet,Y
wavenet: a generative model for raw audio,https://github.com/peustr/wavenet,Y
natural tts synthesis by conditioning wavenet on mel spectrogram predictions,https://github.com/Rayhane-mamah/Tacotron-2,N
natural tts synthesis by conditioning wavenet on mel spectrogram predictions,https://github.com/anandaswarup/seq2seq_speech_synthesis,N
natural tts synthesis by conditioning wavenet on mel spectrogram predictions,https://github.com/NVIDIA/tacotron2,N
natural tts synthesis by conditioning wavenet on mel spectrogram predictions,https://github.com/codetendolkar/tacotron-2-explained,N
tacotron: towards end-to-end speech synthesis,https://github.com/keithito/tacotron,Y
tacotron: towards end-to-end speech synthesis,https://github.com/mozilla/TTS,Y
tacotron: towards end-to-end speech synthesis,https://github.com/vohoaiviet/voice-vector,Y
tacotron: towards end-to-end speech synthesis,https://github.com/mindmapper15/Voice-Converter,Y
tacotron: towards end-to-end speech synthesis,https://github.com/r9y9/tacotron_pytorch,Y
tacotron: towards end-to-end speech synthesis,https://github.com/shortpoet/Final-Project,N
tacotron: towards end-to-end speech synthesis,https://github.com/cchinchristopherj/Tacotron,Y
tacotron: towards end-to-end speech synthesis,https://github.com/0fengzi0/tacotron,Y
tacotron: towards end-to-end speech synthesis,https://github.com/anandaswarup/seq2seq_speech_synthesis,N
tacotron: towards end-to-end speech synthesis,https://github.com/cchinchristopherj/Concert-of-Whales,Y
tacotron: towards end-to-end speech synthesis,https://github.com/andabi/voice-vector,Y
tacotron: towards end-to-end speech synthesis,https://github.com/racinmat/lecture-generator,Y
turing at semeval-2017 task 8: sequential approach to rumour stance classification with branch-lstm,https://github.com/seongjinpark-88/RumorEval2019,N
casenet: deep category-aware semantic edge detection,https://github.com/Chrisding/seal,Y
casenet: deep category-aware semantic edge detection,https://github.com/Chrisding/sbd-preprocess,Y
casenet: deep category-aware semantic edge detection,https://github.com/Chrisding/cityscapes-preprocess,Y
catena: causal and temporal relation extraction from natural language texts,https://github.com/paramitamirza/CATENA,N
simplifying graph convolutional networks,https://github.com/Tiiiger/SGC,Y
simplifying graph convolutional networks,https://github.com/stellargraph/stellargraph,Y
graph convolution over pruned dependency trees improves relation extraction,https://github.com/qipeng/gcn-over-pruned-trees,Y
position-aware attention and supervised data improve slot filling,https://github.com/yuhaozhang/tacred-relation,N
scibert: pretrained contextualized embeddings for scientific text,https://github.com/allenai/scibert,Y
pyramid scene parsing network,https://github.com/tensorflow/models/tree/master/research/deeplab,Y
pyramid scene parsing network,https://github.com/Lxrd-AJ/Advanced_ML,Y
pyramid scene parsing network,https://github.com/reymondzzzz/fast_scnn,N
pyramid scene parsing network,https://github.com/RituYadav92/Image-segmentation,Y
pyramid scene parsing network,https://github.com/RRoundTable/U_net,Y
pyramid scene parsing network,https://github.com/udacity/MLND-CN-Capstone-TGSImage,Y
pyramid scene parsing network,https://github.com/switchablenorms/SwitchNorm_Segmentation,Y
pyramid scene parsing network,https://github.com/warmspringwinds/pytorch-segmentation-detection,Y
pyramid scene parsing network,https://github.com/BOBrown/deeparsing-master,Y
pyramid scene parsing network,https://github.com/kazuto1011/pspnet-pytorch,Y
pyramid scene parsing network,https://github.com/hszhao/PSPNet,Y
pyramid scene parsing network,https://github.com/osmr/imgclsmob,Y
pyramid scene parsing network,https://github.com/leemathew1998/RG,Y
bisenet: bilateral segmentation network for real-time semantic segmentation,https://github.com/zhixuanli/semantic-segmentation-pytorch-1,N
bisenet: bilateral segmentation network for real-time semantic segmentation,https://github.com/Shuai-Xie/BiSeNet-CCP,N
bisenet: bilateral segmentation network for real-time semantic segmentation,https://github.com/GuangyanZhang/SCNN-Deeplabv3-bisenet-icnet,N
bisenet: bilateral segmentation network for real-time semantic segmentation,https://github.com/GuangyanZhang/Paddle-Paddle_SCNN-Deeplabv3-bisenet-icnet,N
bisenet: bilateral segmentation network for real-time semantic segmentation,https://github.com/kirilcvetkov92/Semantic-Segmentation,N
bisenet: bilateral segmentation network for real-time semantic segmentation,https://github.com/Blaizzy/BiSeNet-Implementation,N
bisenet: bilateral segmentation network for real-time semantic segmentation,https://github.com/ycszen/TorchSeg,Y
icnet for real-time semantic segmentation on high-resolution images,https://github.com/GuangyanZhang/Paddle-Paddle_SCNN-Deeplabv3-bisenet-icnet,Y
icnet for real-time semantic segmentation on high-resolution images,https://github.com/hszhao/ICNet,N
icnet for real-time semantic segmentation on high-resolution images,https://github.com/hellochick/ICNet-tensorflow,Y
icnet for real-time semantic segmentation on high-resolution images,https://github.com/GuangyanZhang/SCNN-Deeplabv3-bisenet-icnet,Y
multi-scale context aggregation by dilated convolutions,https://github.com/Kengstar/blogs_and_papers,N
multi-scale context aggregation by dilated convolutions,https://github.com/keillernogueira/FDSI,Y
multi-scale context aggregation by dilated convolutions,https://github.com/srihari-humbarwadi/Multi-Scale-Context-Aggregation-by-Dilated-Convolutions,Y
multi-scale context aggregation by dilated convolutions,https://github.com/vlievin/Unet,Y
multi-scale context aggregation by dilated convolutions,https://github.com/fyu/dilation,Y
multi-scale context aggregation by dilated convolutions,https://github.com/Entodi/meshnet-pytorch,Y
multi-scale context aggregation by dilated convolutions,https://github.com/Wanger-SJTU/FCN-in-the-wild,Y
semantic image segmentation with deep convolutional nets and fully connected crfs,https://github.com/BardOfCodes/pytorch_deeplab_large_fov,N
semantic image segmentation with deep convolutional nets and fully connected crfs,https://github.com/tensorflow/models/tree/master/research/deeplab,N
semantic image segmentation with deep convolutional nets and fully connected crfs,https://github.com/TheLegendAli/DeepLab-Context,N
semantic image segmentation with deep convolutional nets and fully connected crfs,https://github.com/arahusky/Tensorflow-Segmentation,N
semantic image segmentation with deep convolutional nets and fully connected crfs,https://github.com/nightrome/cocostuff10k,N
semantic image segmentation with deep convolutional nets and fully connected crfs,https://github.com/open-cv/deeplab-v1,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/RajkumarPreetham/Road-scene-understanding,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/neuropoly/multiclass-segmentation,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/yinanzhu12/SegNet-keras-implementation,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/s9mondal9upriti/Segnet,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/arahusky/Tensorflow-Segmentation,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/navganti/SegNet,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/DarkGeekMS/Semantic_Segmentation_Models_Keras,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/rotemgoren/segNet,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/TqDavid/td,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/falreis/segmentation-eval,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/ankit-ai/GAN_breast_mammography_segmentation,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/resonance20/segmenter,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/alexgkendall/caffe-segnet,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/Tez01/SegNet-Keras-Implementation,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/alejandrodebus/SegNet,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/navganti/SIVO,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/alexgkendall/SegNet-Tutorial,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/yinanzhu12/SegNet-keras,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/preddy5/segnet,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/Lkruitwagen/remote_sensing_solar_pv,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/assiaben/segnet,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/TimoSaemann/caffe-segnet-cudnn5,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/tkuanlun350/Tensorflow-SegNet,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/ArkaJU/SegNet---Chromosome,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/alexandrelewin/FollowMe,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/JosephPB/XNet,N
segnet: a deep convolutional encoder-decoder architecture for image segmentation,https://github.com/Zhanghongbin-github/SegNet-Tutorial,N
in defense of pre-trained imagenet architectures for real-time semantic segmentation of road-driving images,https://github.com/orsic/swiftnet,N
full-resolution residual networks for semantic segmentation in street scenes,https://github.com/geodekid/frnn,Y
full-resolution residual networks for semantic segmentation in street scenes,https://github.com/robin-chan/decision-rules,N
full-resolution residual networks for semantic segmentation in street scenes,https://github.com/jcheunglin/Full-Resolution-Residual-Networks-with-PyTorch,N
full-resolution residual networks for semantic segmentation in street scenes,https://github.com/TobyPDE/FRRN,Y
fully convolutional networks for semantic segmentation,https://github.com/SDMrFeng/quiz-w10-fcn,Y
fully convolutional networks for semantic segmentation,https://github.com/cooparation/FCN_play,Y
fully convolutional networks for semantic segmentation,https://github.com/sagieppel/Focusing-attention-of-Fully-convolutional-neural-networks-on-Region-of-interest-ROI-input-map-,Y
fully convolutional networks for semantic segmentation,https://github.com/pessimiss/ai100-w9-master,Y
fully convolutional networks for semantic segmentation,https://github.com/resonance20/segmenter,Y
fully convolutional networks for semantic segmentation,https://github.com/SophiaYuSophiaYu/FCN_SemanticSegmentation,Y
fully convolutional networks for semantic segmentation,https://github.com/yeLer/fcn,Y
fully convolutional networks for semantic segmentation,https://github.com/windar427/W10_work,Y
fully convolutional networks for semantic segmentation,https://github.com/Lxrd-AJ/Advanced_ML,Y
fully convolutional networks for semantic segmentation,https://github.com/zhuyi55/week10,Y
fully convolutional networks for semantic segmentation,https://github.com/tsixta/jnet,Y
fully convolutional networks for semantic segmentation,https://github.com/demul/image_segmentation_project,Y
fully convolutional networks for semantic segmentation,https://github.com/LeeMax117/FCN_8s,N
fully convolutional networks for semantic segmentation,https://github.com/waspinator/deep-learning-explorer,Y
fully convolutional networks for semantic segmentation,https://github.com/stoensin/w10-cnn-SegmentationClass,Y
fully convolutional networks for semantic segmentation,https://github.com/geodekid/FCN,Y
fully convolutional networks for semantic segmentation,https://github.com/DaiLisen/Eye-gaze-Point-Detection-Modified-sec-,Y
fully convolutional networks for semantic segmentation,https://github.com/andyzeng/apc-vision-toolbox,Y
fully convolutional networks for semantic segmentation,https://github.com/sagieppel/Fully-convolutional-neural-network-FCN-for-semantic-segmentation-Tensorflow-implementation,Y
fully convolutional networks for semantic segmentation,https://github.com/jiny2001/dcscn-super-resolution,N
fully convolutional networks for semantic segmentation,https://github.com/PuchatekwSzortach/voc_fcn,N
fully convolutional networks for semantic segmentation,https://github.com/hitukensinn/quiz-w9-code,Y
fully convolutional networks for semantic segmentation,https://github.com/petko-nikolov/pysemseg,N
fully convolutional networks for semantic segmentation,https://github.com/linxi159/FCN-caffe,Y
fully convolutional networks for semantic segmentation,https://github.com/shenshutao/image_segmentation,Y
fully convolutional networks for semantic segmentation,https://github.com/Muniuliuma/FCN_8s,Y
fully convolutional networks for semantic segmentation,https://github.com/quanwei309/quiz10,Y
fully convolutional networks for semantic segmentation,https://github.com/LeeMax117/week10_homework,N
fully convolutional networks for semantic segmentation,https://github.com/aolsenjazz/tf-fcn,Y
fully convolutional networks for semantic segmentation,https://github.com/TianchengQ/FCN,Y
fully convolutional networks for semantic segmentation,https://github.com/osmr/imgclsmob,Y
fully convolutional networks for semantic segmentation,https://github.com/fmahoudeau/fcn,Y
fully convolutional networks for semantic segmentation,https://github.com/colorfulxd/WK10_FCN,Y
fully convolutional networks for semantic segmentation,https://github.com/fxfviolet/FCN_for_segmantic_segmentation,Y
fully convolutional networks for semantic segmentation,https://github.com/shelhamer/fcn.berkeleyvision.org,Y
fully convolutional networks for semantic segmentation,https://github.com/lanthlove/segmentation-fcn,N
fully convolutional networks for semantic segmentation,https://github.com/muramasa8191/DeepLearning,Y
fully convolutional networks for semantic segmentation,https://github.com/martinkersner/py_img_seg_eval,Y
fully convolutional networks for semantic segmentation,https://github.com/xiexiexiaoxiexie/Udacity-self-driving-engineer-P2-Advanced-Lane-Finding,Y
fully convolutional networks for semantic segmentation,https://github.com/GodPater/model_fcn,Y
the lovász-softmax loss: a tractable surrogate for the optimization of the intersection-over-union measure in neural networks,https://github.com/bermanmaxim/LovaszSoftmax,N
conditional random fields as recurrent neural networks,https://github.com/MiguelMonteiro/CRFasRNNLayer,Y
conditional random fields as recurrent neural networks,https://github.com/liyin2015/superpixel_crfasrnn,Y
conditional random fields as recurrent neural networks,https://github.com/sadeepj/crfasrnn_keras,Y
conditional random fields as recurrent neural networks,https://github.com/shihui2010/portrait_matting,Y
conditional random fields as recurrent neural networks,https://github.com/shuodata/keras_tensorflow_rnn,Y
conditional random fields as recurrent neural networks,https://github.com/torrvision/crfasrnn,Y
conditional random fields as recurrent neural networks,https://github.com/AsafBarZvi/Liver_project,Y
enet: a deep neural network architecture for real-time semantic segmentation,https://github.com/vaibhavpec2012/ECE1512-Digital-Image-Processing-Homework-3,N
enet: a deep neural network architecture for real-time semantic segmentation,https://github.com/srihari-humbarwadi/ENet-A-Deep-Neural-Network-Architecture-for-Real-Time-Semantic-Segmentation,N
enet: a deep neural network architecture for real-time semantic segmentation,https://github.com/wutianyiRosun/CGNet,N
enet: a deep neural network architecture for real-time semantic segmentation,https://github.com/vaibhavpec2012/ECE1512_Assignment-3,N
enet: a deep neural network architecture for real-time semantic segmentation,https://github.com/sacmehta/ESPNet,N
enet: a deep neural network architecture for real-time semantic segmentation,https://github.com/TimoSaemann/ENet,N
enet: a deep neural network architecture for real-time semantic segmentation,https://github.com/adrshm91/segmentationEnet,N
enet: a deep neural network architecture for real-time semantic segmentation,https://github.com/e-lab/ENet-training,N
enet: a deep neural network architecture for real-time semantic segmentation,https://github.com/klintan/lanenet-pytorch,N
enet: a deep neural network architecture for real-time semantic segmentation,https://github.com/m273033/masters_thesis,N
enet: a deep neural network architecture for real-time semantic segmentation,https://github.com/srihari-humbarwadi/RefineNet-Multi-Path-Refinement-Networks-for-High-Resolution-Semantic-Segmentation,N
enet: a deep neural network architecture for real-time semantic segmentation,https://github.com/iArunava/ENet-Real-Time-Semantic-Segmentation,N
enet: a deep neural network architecture for real-time semantic segmentation,https://github.com/fregu856/segmentation,N
enet: a deep neural network architecture for real-time semantic segmentation,https://github.com/Kaustav97/Colab,N
enet: a deep neural network architecture for real-time semantic segmentation,https://github.com/kwotsin/TensorFlow-ENet,N
3dmv: joint 3d-multi-view prediction for 3d semantic scene segmentation,https://github.com/angeladai/3DMV,Y
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/fxia22/pointnet.pytorch,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/abdullahozer11/Segmentation-and-Classification-of-Objects-in-Point-Clouds,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/ytng001/sensemaking,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/Fnjn/UCSD-CSE-291I,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/YanWei123/Pointnet_encoder_Foldingnet_decoder_quantization,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/hnVfly/pointnet.mxnet,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/LebronGG/PointNet,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/PaParaZz1/PointNet,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/lingzhang1/pointnet_tensorflow,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/YanWei123/PointNet-encoder-and-FoldingNet-decoder-add-quantization-change-latent-code-size-from-512-to-1024,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/freddieee/pn_6d_single,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/ahmed-anas/thesis-pointnet,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/ftdlyc/pointnet_pytorch,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/maggie0106/Graph-CNN-in-3D-Point-Cloud-Classification,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/YanWei123/Pytorch-implementation-of-FoldingNet-encoder-and-decoder-with-graph-pooling-covariance-add-quanti,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/sanantoniochili/PointCloud_KNN,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/hero9968/PointNet-3D-Segmentation,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/xurui1217/pointnet.pytorch-master,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/sarthakTUM/roofn3d,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/charlesq34/pointnet,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/MEIXuYan/PointNet-Relation,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/yanxp/PointNet,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/ZhihaoZhu/PointNet-Implementation-Tensorflow,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/minhncedutw/pointnet1_keras,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/romaintha/pytorch_pointnet,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/Lw510107/pointnet-2018.6.27-,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/coconutzs/PointNet_zs,N
pointnet: deep learning on point sets for 3d classification and segmentation,https://github.com/lingzhang1/pointnet_pytorch,N
pointconv: deep convolutional networks on 3d point clouds,https://github.com/DylanWusee/pointconv,Y
spidercnn: deep learning on point sets with parameterized convolutional filters,https://github.com/xyf513/SpiderCNN,N
splatnet: sparse lattice networks for point cloud processing,https://github.com/NVlabs/splatnet,Y
splatnet: sparse lattice networks for point cloud processing,https://github.com/IsaacRe/splatnet,Y
pointnet++: deep hierarchical feature learning on point sets in a metric space,https://github.com/brbzjl/pointnet2,N
pointnet++: deep hierarchical feature learning on point sets in a metric space,https://github.com/xurui1217/pointnet2-master,N
pointnet++: deep hierarchical feature learning on point sets in a metric space,https://github.com/wangsc1912/mywork,N
pointnet++: deep hierarchical feature learning on point sets in a metric space,https://github.com/charlesq34/pointnet2,N
pointnet++: deep hierarchical feature learning on point sets in a metric space,https://github.com/Leerw/pointnet2,N
pointnet++: deep hierarchical feature learning on point sets in a metric space,https://github.com/houseleo/pointnet,N
pointnet++: deep hierarchical feature learning on point sets in a metric space,https://github.com/Leerw/pointnet2-1,N
pointnet++: deep hierarchical feature learning on point sets in a metric space,https://github.com/lelouedec/3DNetworksPytorch,N
pointnet++: deep hierarchical feature learning on point sets in a metric space,https://github.com/LinZhuoChen/pointnet2_multi_gpu,N
pointnet++: deep hierarchical feature learning on point sets in a metric space,https://github.com/tonysy/pointnet2_tf,N
pointnet++: deep hierarchical feature learning on point sets in a metric space,https://github.com/ftdlyc/pointnet_pytorch,N
pointnet++: deep hierarchical feature learning on point sets in a metric space,https://github.com/liuxinhai/Point2Sequence,N
pointnet++: deep hierarchical feature learning on point sets in a metric space,https://github.com/Lw510107/PointNet,N
escape from cells: deep kd-networks for the recognition of 3d point cloud models,https://github.com/fxia22/kdnet.pytorch,N
escape from cells: deep kd-networks for the recognition of 3d point cloud models,https://github.com/qq456cvb/KdNet-Tensorflow,N
submanifold sparse convolutional networks,https://github.com/uber/sbnet,N
submanifold sparse convolutional networks,https://github.com/facebookresearch/SparseConvNet,Y
3d point-capsule networks,https://github.com/yongheng1991/3D-point-capsule-networks,N
invariant information clustering for unsupervised image classification and segmentation,https://github.com/tareshsethi/querying-reddit,N
invariant information clustering for unsupervised image classification and segmentation,https://github.com/xu-ji/IIC,N
efficient yet deep convolutional neural networks for semantic segmentation,https://github.com/SharifAmit/DilatedFCNSegmentation,N
encoder-decoder with atrous separable convolution for semantic image segmentation,https://github.com/tensorflow/models/tree/master/research/deeplab,Y
encoder-decoder with atrous separable convolution for semantic image segmentation,https://github.com/kekeller/semantic_soy_deeplabv3plus,N
encoder-decoder with atrous separable convolution for semantic image segmentation,https://github.com/parachutel/deeplabv3plus_on_Mapillary_Vistas,N
encoder-decoder with atrous separable convolution for semantic image segmentation,https://github.com/chenmengyang/rename_later,N
encoder-decoder with atrous separable convolution for semantic image segmentation,https://github.com/jfzhang95/pytorch-deeplab-xception,N
encoder-decoder with atrous separable convolution for semantic image segmentation,https://github.com/GuangyanZhang/Paddle-Paddle_SCNN-Deeplabv3-bisenet-icnet,N
encoder-decoder with atrous separable convolution for semantic image segmentation,https://github.com/Vignesh-95/cnn-semantic-segmentation-satellite-images,N
encoder-decoder with atrous separable convolution for semantic image segmentation,https://github.com/sharifelguindi/DeepLab,N
encoder-decoder with atrous separable convolution for semantic image segmentation,https://github.com/tantara/JejuNet,N
encoder-decoder with atrous separable convolution for semantic image segmentation,https://github.com/AutomatedAI/deeplab_inference,N
encoder-decoder with atrous separable convolution for semantic image segmentation,https://github.com/Syarujianai/deeplab-commented,N
encoder-decoder with atrous separable convolution for semantic image segmentation,https://github.com/petko-nikolov/pysemseg,N
encoder-decoder with atrous separable convolution for semantic image segmentation,https://github.com/shenshutao/image_segmentation,N
encoder-decoder with atrous separable convolution for semantic image segmentation,https://github.com/GuangyanZhang/SCNN-Deeplabv3-bisenet-icnet,N
encoder-decoder with atrous separable convolution for semantic image segmentation,https://github.com/leimao/DeepLab_v3,N
encoder-decoder with atrous separable convolution for semantic image segmentation,https://github.com/EdwinAlegria/object_semantic_deeplabv3,N
encoder-decoder with atrous separable convolution for semantic image segmentation,https://github.com/AutomatedAI/deeplab,N
encoder-decoder with atrous separable convolution for semantic image segmentation,https://github.com/bonlime/keras-deeplab-v3-plus,N
encoder-decoder with atrous separable convolution for semantic image segmentation,https://github.com/gepettolab/gepetto-deeplab,N
encoder-decoder with atrous separable convolution for semantic image segmentation,https://github.com/Robinatp/Deeplab_Tensorflow,N
encoder-decoder with atrous separable convolution for semantic image segmentation,https://github.com/heidongxianhau/deeplab2,N
rethinking atrous convolution for semantic image segmentation,https://github.com/chenmengyang/rename_later,N
rethinking atrous convolution for semantic image segmentation,https://github.com/tensorflow/models/tree/master/research/deeplab,Y
rethinking atrous convolution for semantic image segmentation,https://github.com/HqWei/Semantic-Segmentation-with-Full-Convolutional-Neural-Network,Y
rethinking atrous convolution for semantic image segmentation,https://github.com/EdwinAlegria/object_semantic_deeplabv3,Y
rethinking atrous convolution for semantic image segmentation,https://github.com/AutomatedAI/deeplab,N
rethinking atrous convolution for semantic image segmentation,https://github.com/kekeller/semantic_soy_deeplabv3plus,Y
rethinking atrous convolution for semantic image segmentation,https://github.com/AutomatedAI/deeplab_inference,Y
rethinking atrous convolution for semantic image segmentation,https://github.com/bonlime/keras-deeplab-v3-plus,N
rethinking atrous convolution for semantic image segmentation,https://github.com/udacity/MLND-CN-Capstone-TGSImage,Y
rethinking atrous convolution for semantic image segmentation,https://github.com/DarkGeekMS/Semantic_Segmentation_Models_Keras,Y
rethinking atrous convolution for semantic image segmentation,https://github.com/Robinatp/Deeplab_Tensorflow,Y
rethinking atrous convolution for semantic image segmentation,https://github.com/gepettolab/gepetto-deeplab,Y
rethinking atrous convolution for semantic image segmentation,https://github.com/Syarujianai/deeplab-commented,Y
rethinking atrous convolution for semantic image segmentation,https://github.com/osmr/imgclsmob,Y
rethinking atrous convolution for semantic image segmentation,https://github.com/leimao/DeepLab_v3,Y
rethinking atrous convolution for semantic image segmentation,https://github.com/parachutel/deeplabv3plus_on_Mapillary_Vistas,Y
rethinking atrous convolution for semantic image segmentation,https://github.com/heidongxianhau/deeplab2,Y
rethinking atrous convolution for semantic image segmentation,https://github.com/Vignesh-95/cnn-semantic-segmentation-satellite-images,Y
rethinking atrous convolution for semantic image segmentation,https://github.com/sharifelguindi/DeepLab,Y
learning a discriminative feature network for semantic segmentation,https://github.com/YuhuiMa/DFN-tensorflow,Y
learning a discriminative feature network for semantic segmentation,https://github.com/zhixuanli/semantic-segmentation-pytorch-1,N
learning a discriminative feature network for semantic segmentation,https://github.com/ycszen/TorchSeg,Y
context encoding for semantic segmentation,https://github.com/zhanghang1989/PyTorch-Encoding,Y
context encoding for semantic segmentation,https://github.com/CWanli/myencoding,Y
context encoding for semantic segmentation,https://github.com/zhusiling/EncNet,Y
context encoding for semantic segmentation,https://github.com/kmaninis/pytorch-encoding,Y
wider or deeper: revisiting the resnet model for visual recognition,https://github.com/itijyou/ademxapp,Y
wider or deeper: revisiting the resnet model for visual recognition,https://github.com/ZhaoJ9014/shallower-wider-ResNet-Model-A-SW-ResNet-A,Y
refinenet: multi-path refinement networks for high-resolution semantic segmentation,https://github.com/Attila94/refinenet-keras,N
refinenet: multi-path refinement networks for high-resolution semantic segmentation,https://github.com/DrSleep/refinenet-pytorch,N
refinenet: multi-path refinement networks for high-resolution semantic segmentation,https://github.com/guosheng/refinenet,N
refinenet: multi-path refinement networks for high-resolution semantic segmentation,https://github.com/oravus/lostX,N
large kernel matters -- improve semantic segmentation by global convolutional network,https://github.com/preritj/segmentation,N
understanding convolution for semantic segmentation,https://github.com/modelhub-ai/duc-semantic,Y
understanding convolution for semantic segmentation,https://github.com/TuSimple/TuSimple-DUC,Y
understanding convolution for semantic segmentation,https://github.com/leemathew1998/RG,Y
"deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs",https://github.com/kdethoor/panoptictorch,N
"deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs",https://github.com/tensorflow/models/tree/master/research/deeplab,N
"deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs",https://github.com/Lxrd-AJ/Advanced_ML,N
"deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs",https://github.com/liarba/caffe_dev,N
"deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs",https://github.com/RituYadav92/Image-segmentation,N
"deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs",https://github.com/violin0847/crowdcounting,N
"deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs",https://github.com/cgsaxner/UB_Segmentation,N
"deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs",https://github.com/switchablenorms/SwitchNorm_Segmentation,N
"deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs",https://github.com/z01nl1o02/deeplab-v2,N
"deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs",https://github.com/kazuto1011/deeplab-pytorch,N
"deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs",https://github.com/yaq007/Autofocus-Layer,N
"deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs",https://github.com/warmspringwinds/pytorch-segmentation-detection,N
"deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs",https://github.com/purushothamgowthu/deep-photo-styletransfer,N
"deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs",https://github.com/cdmh/deeplab-public-ver2,N
"deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs",https://github.com/keb123keb/deeplabv2,N
"deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs",https://github.com/open-cv/deeplab-v2,N
"deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs",https://github.com/OIdiotLin/DeepLab-pytorch,N
"deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs",https://github.com/leimao/DeepLab_v3,N
"deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs",https://github.com/isht7/pytorch-deeplab-resnet,N
"deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs",https://github.com/ShichengChen/WaveUNet,N
the lovász-softmax loss: a tractable surrogate for the optimization of the intersection-over-union measure in neural networks,https://github.com/bermanmaxim/LovaszSoftmax,N
revisiting unreasonable effectiveness of data in deep learning era,https://github.com/wjizhong/MachineLearning,Y
revisiting unreasonable effectiveness of data in deep learning era,https://github.com/Tencent/tencent-ml-images,Y
parsenet: looking wider to see better,https://github.com/xiamenwcy/extended-caffe,Y
parsenet: looking wider to see better,https://github.com/tensorflow/models/tree/master/research/deeplab,Y
the one hundred layers tiramisu: fully convolutional densenets for semantic segmentation,https://github.com/smdYe/FC-DenseNet-Keras,N
the one hundred layers tiramisu: fully convolutional densenets for semantic segmentation,https://github.com/pattyhendrix/CamVid-95-accuracy,N
the one hundred layers tiramisu: fully convolutional densenets for semantic segmentation,https://github.com/petko-nikolov/pysemseg,N
the one hundred layers tiramisu: fully convolutional densenets for semantic segmentation,https://github.com/demul/image_segmentation_project,N
the one hundred layers tiramisu: fully convolutional densenets for semantic segmentation,https://github.com/Kaido0/Brain-Tissue-Segment-Keras,N
the one hundred layers tiramisu: fully convolutional densenets for semantic segmentation,https://github.com/Septembit/Image-segmentation,N
the one hundred layers tiramisu: fully convolutional densenets for semantic segmentation,https://github.com/SimJeg/FC-DenseNet,N
the one hundred layers tiramisu: fully convolutional densenets for semantic segmentation,https://github.com/0bserver07/One-Hundred-Layers-Tiramisu,N
the one hundred layers tiramisu: fully convolutional densenets for semantic segmentation,https://github.com/khiemkhanh98/ColabTiramisu,N
the one hundred layers tiramisu: fully convolutional densenets for semantic segmentation,https://github.com/ankit-vaghela30/Cilia-Segmentation,N
the one hundred layers tiramisu: fully convolutional densenets for semantic segmentation,https://github.com/asprenger/keras_fc_densenet,N
reseg: a recurrent neural network-based model for semantic segmentation,https://github.com/SConsul/ReSeg,Y
reseg: a recurrent neural network-based model for semantic segmentation,https://github.com/fvisin/reseg,N
fastfcn: rethinking dilated convolution in the backbone for semantic segmentation,https://github.com/wuhuikai/FastFCN,Y
sgpn: similarity group proposal network for 3d point cloud instance segmentation,https://github.com/laughtervv/SGPN,N
sgpn: similarity group proposal network for 3d point cloud instance segmentation,https://github.com/Sekunde/sgpn,N
in-place activated batchnorm for memory-optimized training of dnns,https://github.com/mapillary/inplace_abn,Y
in-place activated batchnorm for memory-optimized training of dnns,https://github.com/ternaus/TernausNetV2,Y
ocnet: object context network for scene parsing,https://github.com/PkuRainBow/OCNet,Y
ocnet: object context network for scene parsing,https://github.com/PkuRainBow/OCNet.pytorch,Y
dual attention network for scene segmentation,https://github.com/junfu1115/DANet,Y
dual attention network for scene segmentation,https://github.com/hbzhang/AwesomeSelfDriving,Y
dual attention network for scene segmentation,https://github.com/zhenxingsh/Pytorch_DANet,Y
dual attention network for scene segmentation,https://github.com/yougoforward/hlzhu_DANet_git,Y
decoders matter for semantic segmentation: data-dependent decoding enables flexible feature aggregation,https://github.com/LinZhuoChen/DUpsampling,N
self-produced guidance for weakly-supervised object localization,https://github.com/xiaomengyc/SPG,N
frustum pointnets for 3d object detection from rgb-d data,https://github.com/charlesq34/frustum-pointnets,Y
frustum pointnets for 3d object detection from rgb-d data,https://github.com/abdullahozer11/Segmentation-and-Classification-of-Objects-in-Point-Clouds,Y
frustum pointnets for 3d object detection from rgb-d data,https://github.com/ytng001/sensemaking,N
frustum pointnets for 3d object detection from rgb-d data,https://github.com/Leerw/pointnet2,Y
frustum pointnets for 3d object detection from rgb-d data,https://github.com/LebronGG/PointNet,N
frustum pointnets for 3d object detection from rgb-d data,https://github.com/lingzhang1/pointnet_tensorflow,Y
frustum pointnets for 3d object detection from rgb-d data,https://github.com/voidrank/Geo-CNN,N
frustum pointnets for 3d object detection from rgb-d data,https://github.com/tonysy/pointnet2_tf,Y
frustum pointnets for 3d object detection from rgb-d data,https://github.com/chonepieceyb/reading-frustum-pointnets-code,Y
frustum pointnets for 3d object detection from rgb-d data,https://github.com/ahmed-anas/thesis-pointnet,Y
frustum pointnets for 3d object detection from rgb-d data,https://github.com/xurui1217/pointnet2-master,Y
frustum pointnets for 3d object detection from rgb-d data,https://github.com/tancik/fpointnets_image_features,Y
frustum pointnets for 3d object detection from rgb-d data,https://github.com/kargarisaac/3d-MNIST-classification-PointNet,Y
frustum pointnets for 3d object detection from rgb-d data,https://github.com/hero9968/PointNet-3D-Segmentation,Y
frustum pointnets for 3d object detection from rgb-d data,https://github.com/kargarisaac/PointNet-SemSeg-VKITTI3D,Y
frustum pointnets for 3d object detection from rgb-d data,https://github.com/charlesq34/pointnet,Y
frustum pointnets for 3d object detection from rgb-d data,https://github.com/MEIXuYan/PointNet-Relation,N
frustum pointnets for 3d object detection from rgb-d data,https://github.com/Lw510107/PointNet,Y
frustum pointnets for 3d object detection from rgb-d data,https://github.com/brbzjl/pointnet2,Y
frustum pointnets for 3d object detection from rgb-d data,https://github.com/Yc174/frustum-pointnets,Y
frustum pointnets for 3d object detection from rgb-d data,https://github.com/wangsc1912/mywork,Y
frustum pointnets for 3d object detection from rgb-d data,https://github.com/ZhihaoZhu/PointNet-Implementation-Tensorflow,Y
frustum pointnets for 3d object detection from rgb-d data,https://github.com/charlesq34/pointnet2,Y
frustum pointnets for 3d object detection from rgb-d data,https://github.com/houseleo/pointnet,Y
frustum pointnets for 3d object detection from rgb-d data,https://github.com/Lw510107/pointnet-2018.6.27-,Y
frustum pointnets for 3d object detection from rgb-d data,https://github.com/Leerw/pointnet2-1,Y
frustum pointnets for 3d object detection from rgb-d data,https://github.com/coconutzs/PointNet_zs,Y
frustum pointnets for 3d object detection from rgb-d data,https://github.com/LinZhuoChen/pointnet2_multi_gpu,N
voxelnet: end-to-end learning for point cloud based 3d object detection,https://github.com/abdullahozer11/Segmentation-and-Classification-of-Objects-in-Point-Clouds,Y
voxelnet: end-to-end learning for point cloud based 3d object detection,https://github.com/ytng001/sensemaking,N
voxelnet: end-to-end learning for point cloud based 3d object detection,https://github.com/coconutzs/PointNet_zs,Y
voxelnet: end-to-end learning for point cloud based 3d object detection,https://github.com/ZhihaoZhu/PointNet-Implementation-Tensorflow,Y
voxelnet: end-to-end learning for point cloud based 3d object detection,https://github.com/kargarisaac/3d-MNIST-classification-PointNet,Y
voxelnet: end-to-end learning for point cloud based 3d object detection,https://github.com/LebronGG/PointNet,N
voxelnet: end-to-end learning for point cloud based 3d object detection,https://github.com/hy-signal/VoxelNet,Y
voxelnet: end-to-end learning for point cloud based 3d object detection,https://github.com/lingzhang1/pointnet_tensorflow,Y
voxelnet: end-to-end learning for point cloud based 3d object detection,https://github.com/kargarisaac/PointNet-SemSeg-VKITTI3D,Y
voxelnet: end-to-end learning for point cloud based 3d object detection,https://github.com/Lw510107/pointnet-2018.6.27-,Y
voxelnet: end-to-end learning for point cloud based 3d object detection,https://github.com/hero9968/PointNet-3D-Segmentation,Y
voxelnet: end-to-end learning for point cloud based 3d object detection,https://github.com/aaronfriedman6/MV3D_VoxelNet,Y
voxelnet: end-to-end learning for point cloud based 3d object detection,https://github.com/charlesq34/pointnet,Y
voxelnet: end-to-end learning for point cloud based 3d object detection,https://github.com/ahmed-anas/thesis-pointnet,Y
voxelnet: end-to-end learning for point cloud based 3d object detection,https://github.com/MEIXuYan/PointNet-Relation,N
multimodal sentiment analysis using hierarchical fusion with context modeling,https://github.com/SenticNet/hfusion,N
context-dependent sentiment analysis in user-generated videos,https://github.com/soujanyaporia/multimodal-sentiment-analysis,N
context-dependent sentiment analysis in user-generated videos,https://github.com/senticnet/sc-lstm,N
dialoguernn: an attentive rnn for emotion detection in conversations,https://github.com/SenticNet/conv-emotion,N
icon: interactive conversational memory network for multimodal emotion detection,https://github.com/SenticNet/conv-emotion,N
conversational memory network for emotion recognition in dyadic dialogue videos,https://github.com/SenticNet/conv-emotion,N
v2v-posenet: voxel-to-voxel prediction network for accurate 3d hand and human pose estimation from a single depth map,https://github.com/dragonbook/V2V-PoseNet-pytorch,Y
v2v-posenet: voxel-to-voxel prediction network for accurate 3d hand and human pose estimation from a single depth map,https://github.com/mks0601/V2V-PoseNet_RELEASE,Y
v2v-posenet: voxel-to-voxel prediction network for accurate 3d hand and human pose estimation from a single depth map,https://github.com/pengfeiZhao1993/V2V-PoseNet,Y
pose guided structured region ensemble network for cascaded hand pose estimation,https://github.com/xinghaochen/Pose-REN,N
dense 3d regression for hand pose estimation,https://github.com/melonwan/denseReg,Y
region ensemble network: improving convolutional network for hand pose estimation,https://github.com/guohengkai/region-ensemble-network,N
deepprior++: improving fast and accurate 3d hand pose estimation,https://github.com/moberweger/deep-prior-pp,N
deepprior++: improving fast and accurate 3d hand pose estimation,https://github.com/mks0601/V2V-PoseNet_RELEASE,Y
deepprior++: improving fast and accurate 3d hand pose estimation,https://github.com/pengfeiZhao1993/V2V-PoseNet,Y
linguistically-informed self-attention for semantic role labeling,https://github.com/strubell/LISA,Y
deep semantic role labeling: what works and what’s next,https://github.com/luheng/deep_srl,N
a span selection model for semantic role labeling,https://github.com/hiroki13/span-based-srl,Y
"dependency or span, end-to-end uniform semantic role labeling",https://github.com/bcmi220/unisrl,N
deep semantic role labeling with self-attention,https://github.com/XMUNLP/Tagger,Y
universal sentence encoder,https://github.com/facebookresearch/InferSent,Y
universal sentence encoder,https://github.com/contemn1/sentence_embeddings,Y
universal sentence encoder,https://github.com/facebookresearch/SentEval,Y
universal sentence encoder,https://github.com/krisbukovi/document_similarity,Y
universal sentence encoder,https://github.com/ncbi-nlp/BioWordVec,N
universal sentence encoder,https://github.com/domyounglee/text2cypher,Y
universal sentence encoder,https://github.com/ncbi-nlp/BioSentVec,Y
universal sentence encoder,https://github.com/korymath/jann,Y
universal sentence encoder,https://github.com/yfpeng/biosentvec,N
universal sentence encoder,https://github.com/ceshine/textrank_summary_benchmark,Y
spider: a large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task,https://github.com/taoyds/spider,Y
jointly learning to label sentences and tokens,https://github.com/marekrei/mltagger,N
a multilayer convolutional encoder-decoder neural network for grammatical error correction,https://github.com/seaweiqing/image2story,N
a multilayer convolutional encoder-decoder neural network for grammatical error correction,https://github.com/nusnlp/mlconvgec2018,Y
a multilayer convolutional encoder-decoder neural network for grammatical error correction,https://github.com/seaweiqing/neuraltalk_plus_charcnn,N
neural quality estimation of grammatical error correction,https://github.com/nusnlp/neuqe,N
reaching human-level performance in automatic grammatical error correction: an empirical study,https://github.com/getao/human-performance-gec,N
semi-supervised sequence modeling with cross-view training,https://github.com/tensorflow/models,N
deep biaffine attention for neural dependency parsing,https://github.com/ITUnlp/UniParse,Y
deep biaffine attention for neural dependency parsing,https://github.com/tdozat/Parser-v1,Y
deep biaffine attention for neural dependency parsing,https://github.com/februa22/biaffineparser,Y
deep biaffine attention for neural dependency parsing,https://github.com/ganeshjawahar/ELMoLex,Y
deep biaffine attention for neural dependency parsing,https://github.com/chantera/biaffineparser,Y
deep biaffine attention for neural dependency parsing,https://github.com/mgzeke0/biaffineparser,Y
deep biaffine attention for neural dependency parsing,https://github.com/XuezheMax/NeuroNLP2,Y
deep biaffine attention for neural dependency parsing,https://github.com/wanghm92/Sing_Par,Y
deep biaffine attention for neural dependency parsing,https://github.com/makyr90/Biaffine_Parser,Y
deep biaffine attention for neural dependency parsing,https://github.com/dmlc/gluon-nlp,N
deep biaffine attention for neural dependency parsing,https://github.com/tdozat/Parser,Y
globally normalized transition-based neural networks,https://github.com/tensorflow/models/tree/master/research/syntaxnet,Y
an improved neural network model for joint pos tagging and dependency parsing,https://github.com/datquocnguyen/jPTDP,N
simple and accurate dependency parsing using bidirectional lstm feature representations,https://github.com/ITUnlp/UniParse,N
from pos tagging to dependency parsing for biomedical event extraction,https://github.com/datquocnguyen/BioPosDep,Y
from pos tagging to dependency parsing for biomedical event extraction,https://github.com/datquocnguyen/BioNLP,Y
hierarchical attention based position-aware network for aspect-level sentiment analysis,https://github.com/DUT-LiuYang/Aspect-Sentiment-Analysis,N
learning latent opinions for aspect-level sentiment classification,https://github.com/berlino/SA-Sent,N
left-center-right separated neural network for aspect-based sentiment analysis with rotatory attention,https://github.com/NUSTM/ABSC,N
aspect level sentiment classification with attention-over-attention neural networks,https://github.com/songyouwei/ABSA-PyTorch,N
a position-aware bidirectional attention network for aspect-level sentiment analysis,https://github.com/hiyouga/PBAN-PyTorch,N
aspect level sentiment classification with deep memory network,https://github.com/jimmyyfeng/TD-LSTM,N
aspect level sentiment classification with deep memory network,https://github.com/ridakadri14/AspectBasedSentimentAnalysis,N
aspect level sentiment classification with deep memory network,https://github.com/mayurikumari047/Aspect-Based-Sentiment-Analysis-Multiple-Models,N
aspect level sentiment classification with deep memory network,https://github.com/NUSTM/ABSC,N
aspect level sentiment classification with deep memory network,https://github.com/songyouwei/ABSA-PyTorch,N
aspect level sentiment classification with deep memory network,https://github.com/mayurikumari047/Aspect-Based-Sentiment-Analysis,N
transformation networks for target-oriented sentiment classification,https://github.com/lixin4ever/TNet,N
recurrent attention network on memory for aspect sentiment analysis,https://github.com/songyouwei/ABSA-PyTorch,N
recurrent attention network on memory for aspect sentiment analysis,https://github.com/lpq29743/RAM,N
interactive attention networks for aspect-level sentiment classification,https://github.com/songyouwei/ABSA-PyTorch,N
interactive attention networks for aspect-level sentiment classification,https://github.com/tori22/sentiment_torch,N
interactive attention networks for aspect-level sentiment classification,https://github.com/NUSTM/ABSC,N
aspect based sentiment analysis with gated convolutional networks,https://github.com/wxue004cs/GCAE,N
attention-based lstm for aspect-level sentiment classification,https://github.com/jimmyyfeng/TD-LSTM,N
attention-based lstm for aspect-level sentiment classification,https://github.com/songyouwei/ABSA-PyTorch,N
effective lstms for target-dependent sentiment classification,https://github.com/jimmyyfeng/TD-LSTM,N
effective lstms for target-dependent sentiment classification,https://github.com/ridakadri14/AspectBasedSentimentAnalysis,N
effective lstms for target-dependent sentiment classification,https://github.com/bluemonk482/tdparse,N
effective lstms for target-dependent sentiment classification,https://github.com/NUSTM/ABSC,N
effective lstms for target-dependent sentiment classification,https://github.com/hiyouga/PBAN-PyTorch,N
effective lstms for target-dependent sentiment classification,https://github.com/songyouwei/ABSA-PyTorch,N
recurrent entity networks with delayed memory update for targeted aspect-based sentiment analysis,https://github.com/liufly/delayed-memory-update-entnet,N
contextual inter-modal attention for multi-modal sentiment analysis,https://github.com/soujanyaporia/contextual-multimodal-fusion,N
multi-attention recurrent network for human communication comprehension,https://github.com/A2Zadeh/MARN,N
multi-attention recurrent network for human communication comprehension,https://github.com/pliang279/MFN,N
bb_twtr at semeval-2017 task 4: twitter sentiment analysis with cnns and lstms,https://github.com/mihirahlawat/Sentiment-Analysis,N
bb_twtr at semeval-2017 task 4: twitter sentiment analysis with cnns and lstms,https://github.com/saurabhrathor/InceptionModel_SentimentAnalysis,N
bb_twtr at semeval-2017 task 4: twitter sentiment analysis with cnns and lstms,https://github.com/leelaylay/TweetSemEval,N
datastories at semeval-2017 task 4: deep lstm with attention for message-level and topic-based sentiment analysis,https://github.com/cbaziotis/ekphrasis,Y
convolutional neural networks with recurrent neural filters,https://github.com/bloomberg/cnn-rnf,Y
leveraging multi-grained sentiment lexicon information for neural sequence models,https://github.com/zengyan-97/Sentiment-Lexicon,N
"less grammar, more features",https://github.com/dlwh/epic,N
a c-lstm neural network for text classification,https://github.com/EngSalem/TextClassification_Off_the_shelf,Y
a c-lstm neural network for text classification,https://github.com/EngSalem/Text-Classification-of-the-shelf-with-a-normalizer-for-Arabic-text-,Y
a c-lstm neural network for text classification,https://github.com/jingyuanz/telecom_query_classification,N
a c-lstm neural network for text classification,https://github.com/EngSalem/ArabicTextClassification,Y
baseline needs more love: on simple word-embedding-based models and associated pooling mechanisms,https://github.com/dinghanshen/SWEM,Y
baseline needs more love: on simple word-embedding-based models and associated pooling mechanisms,https://github.com/nyk510/scdv-python,N
all-but-the-top: simple and effective postprocessing for word representations,https://github.com/nlpAThits/WOMBAT,N
all-but-the-top: simple and effective postprocessing for word representations,https://github.com/lgalke/vec4ir,N
all-but-the-top: simple and effective postprocessing for word representations,https://github.com/woctezuma/steam-descriptions,N
emo2vec: learning generalized emotion representation by multi-task training,https://github.com/pxuab/emo2vec_wassa_paper,N
universal language model fine-tuning for text classification,https://github.com/AbhimanyuAryan/IMDB-NLP,Y
universal language model fine-tuning for text classification,https://github.com/cstorm125/thai2fit,Y
universal language model fine-tuning for text classification,https://github.com/nextbigwhat-ai/sentiment-analysis-pytorch,N
universal language model fine-tuning for text classification,https://github.com/rania000/SentAnalyser,Y
universal language model fine-tuning for text classification,https://github.com/PrideLee/sentiment-analysis,Y
universal language model fine-tuning for text classification,https://github.com/DhruvDh/semisupervised_nlu,Y
universal language model fine-tuning for text classification,https://github.com/alexandra-chron/wassa-2018,Y
universal language model fine-tuning for text classification,https://github.com/SkullFang/ULMFIT_NLP_Classification,Y
universal language model fine-tuning for text classification,https://github.com/dhruvsawhney/CS152_FinalProject,Y
universal language model fine-tuning for text classification,https://github.com/comicencyclo/TransferLearning_DiscriminativeFineTuning,Y
universal language model fine-tuning for text classification,https://github.com/Socialbird-AILab/BERT-Classification-Tutorial,Y
universal language model fine-tuning for text classification,https://github.com/haianhle/ULMFiT-Sentiment,Y
universal language model fine-tuning for text classification,https://github.com/jannenev/ulmfit-language-model,Y
universal language model fine-tuning for text classification,https://github.com/mamamot/Russian-ULMFit,Y
universal language model fine-tuning for text classification,https://github.com/khumbuai/keras_wiki_lm,Y
universal language model fine-tuning for text classification,https://github.com/MJahangeerQureshi/Text-Classification,N
universal language model fine-tuning for text classification,https://github.com/alexandra-chron/ntua-slp-wassa-iest2018,Y
universal language model fine-tuning for text classification,https://github.com/TheShadow29/subreddit-classification-dataset,Y
universal language model fine-tuning for text classification,https://github.com/anubhavmaity/Ag-News-Category-Classifier,Y
universal language model fine-tuning for text classification,https://github.com/prajjwal1/language-modelling,Y
deep pyramid convolutional neural networks for text categorization,https://github.com/Cheneng/DPCNN,N
compositional coding capsule network with k-means routing for text classification,https://github.com/leftthomas/CCCapsNet,N
joint embedding of words and labels for text classification,https://github.com/ShuanDeMorian/Project_AT_News_Classification,Y
joint embedding of words and labels for text classification,https://github.com/guoyinwang/LEAM,Y
bag of tricks for efficient text classification,https://github.com/salestock/fastText.py,Y
bag of tricks for efficient text classification,https://github.com/vinhkhuc/JFastText,Y
bag of tricks for efficient text classification,https://github.com/xuzhezhaozhao/fastText_reading,Y
bag of tricks for efficient text classification,https://github.com/RaRe-Technologies/gensim-data,Y
bag of tricks for efficient text classification,https://github.com/DW-yejing/fasttext4j-jdk6,Y
bag of tricks for efficient text classification,https://github.com/luhuiguo/jfasttext,Y
bag of tricks for efficient text classification,https://github.com/brightmart/text_classification,Y
bag of tricks for efficient text classification,https://github.com/dmlc/gluon-nlp,N
bag of tricks for efficient text classification,https://github.com/linkfluence/fastText4j,Y
bag of tricks for efficient text classification,https://github.com/bung87/whatlangid,Y
bag of tricks for efficient text classification,https://github.com/ppke-nlpg/fastText_factored-cbow,Y
bag of tricks for efficient text classification,https://github.com/indix/whatthelang,Y
bag of tricks for efficient text classification,https://github.com/joonleesky/DeepLearning_Reimplementations,Y
bag of tricks for efficient text classification,https://github.com/pommedeterresautee/fastrtext,N
bag of tricks for efficient text classification,https://github.com/bzz/LangID,Y
bag of tricks for efficient text classification,https://github.com/graykode/nlp-tutorial,Y
bag of tricks for efficient text classification,https://github.com/facebookresearch/fastText,Y
bag of tricks for efficient text classification,https://github.com/TEAMLAB-Lecture/deep_nlp_101,Y
bag of tricks for efficient text classification,https://github.com/mayabot/fastText4j,Y
bag of tricks for efficient text classification,https://github.com/Nim-NLP/fastText,N
bag of tricks for efficient text classification,https://github.com/Kinetikm/fasttextRelearnExperiment,Y
bag of tricks for efficient text classification,https://github.com/mwydmuch/extremeText,Y
bag of tricks for efficient text classification,https://github.com/bung87/fastText,Y
bag of tricks for efficient text classification,https://github.com/rmenegaux/fastDNA,Y
bag of tricks for efficient text classification,https://github.com/ShreyaKhare/imdb_fasttext,N
bag of tricks for efficient text classification,https://github.com/romik9999/fasttext-1925f09ed3,Y
bag of tricks for efficient text classification,https://github.com/donghyeonk/fastText1607,Y
bag of tricks for efficient text classification,https://github.com/luckyPT/jvm-ml,Y
bag of tricks for efficient text classification,https://github.com/M155K4R4/fastText,Y
character-level convolutional networks for text classification,https://github.com/AskingQ/If-or-If-not,Y
character-level convolutional networks for text classification,https://github.com/lonePatient/char-cnn-text-classification,Y
character-level convolutional networks for text classification,https://github.com/mhjabreel/CharCNN,Y
character-level convolutional networks for text classification,https://github.com/ahmedbesbes/character-based-cnn,Y
character-level convolutional networks for text classification,https://github.com/threelittlemonkeys/cnn-text-classification-pytorch,Y
character-level convolutional networks for text classification,https://github.com/threelittlemonkeys/text-cnn-pytorch,Y
character-level convolutional networks for text classification,https://github.com/zhangxiangxiao/Crepe,Y
character-level convolutional networks for text classification,https://github.com/mhjabreel/CharCnn_Keras,Y
character-level convolutional networks for text classification,https://github.com/HeyLynne/char_cnn,Y
squeezed very deep convolutional neural networks for text classification,https://github.com/lazarotm/SVDCNN,N
gpu kernels for block-sparse weights,https://github.com/openai/blocksparse,N
learning to generate reviews and discovering sentiment,https://github.com/openai/generating-reviews-discovering-sentiment,Y
on the role of text preprocessing in neural network architectures: an evaluation study on text categorization and sentiment analysis,https://github.com/pedrada88/preproc-textclassification,Y
practical text classification with large pre-trained language models,https://github.com/NVIDIA/sentiment-discovery,Y
information aggregation via dynamic routing for sequence encoding,https://github.com/FudanNLP/Capsule4TextClassification,Y
investigating capsule networks with dynamic routing for text classification,https://github.com/andyweizhao/capsule_text_classification,Y
task-oriented word embedding for text classification,https://github.com/qianliu0708/ToWE,N
adversarial training methods for semi-supervised text classification,https://github.com/tensorflow/models/tree/master/research/adversarial_text,Y
adversarial training methods for semi-supervised text classification,https://github.com/TobiasLee/Text-Classification,Y
effective use of word order for text categorization with convolutional neural networks,https://github.com/tensorflow/models/tree/master/research/sentiment_analysis,N
effective use of word order for text categorization with convolutional neural networks,https://github.com/jean-kunz/ml_research_papers,N
long short-term memory with dynamic skip connections,https://github.com/lecholin/DynamicLSTM,Y
graph convolutional networks for text classification,https://github.com/yao8839836/text_gcn,N
graph convolutional networks for text classification,https://github.com/koreyou/text-gcn-chainer,Y
explicit interaction model towards text classification,https://github.com/NonvolatileMemory/AAAI_2019_EXAM,N
sliced recurrent neural networks,https://github.com/zepingyu0512/srnn,N
on tree-based neural sentence modeling,https://github.com/ExplorerFreda/TreeEnc,Y
revisiting distributional correspondence indexing: a python reimplementation and new experiments,https://github.com/AlexMoreo/pydci,N
domain-adversarial training of neural networks,https://github.com/tachitachi/GradientReversal,Y
domain-adversarial training of neural networks,https://github.com/scpark20/universal-music-translation,Y
domain-adversarial training of neural networks,https://github.com/domainadaptation/salad,N
domain-adversarial training of neural networks,https://github.com/vihari/crossgrad,Y
domain-adversarial training of neural networks,https://github.com/asahi417/DeepDomainAdaptation,Y
domain-adversarial training of neural networks,https://github.com/ShichengChen/Domain-Adversarial-Training-of-Neural-Networks,Y
domain-adversarial training of neural networks,https://github.com/erlendd/ddan,Y
domain-adversarial training of neural networks,https://github.com/JorisRoels/domain-adaptive-segmentation,Y
domain-adversarial training of neural networks,https://github.com/ShichengChen/WaveNetSeparateAudio,Y
mixing context granularities for improved entity linking on question answering data across entity categories,https://github.com/UKPLab/starsem2018-entity-linking,N
chinese ner using lattice lstm,https://github.com/jiesutd/LatticeLSTM,N
chinese ner using lattice lstm,https://github.com/Houlong66/lattice_lstm_with_pytorch,N
adversarial transfer learning for chinese named entity recognition with self-attention mechanism,https://github.com/CPF-NLPR/AT4ChineseNER,N
a neural transition-based model for nested mention recognition,https://github.com/berlino/nest-trans-em18,N
fast and accurate entity recognition with iterated dilated convolutions,https://github.com/iesl/dilated-cnn-ner,Y
fast and accurate entity recognition with iterated dilated convolutions,https://github.com/zjuym/chinese_cws_ner,Y
collabonet: collaboration of deep neural networks for biomedical named entity recognition,https://github.com/wonjininfo/CollaboNet,N
evaluating the utility of hand-crafted features in sequence labelling,https://github.com/minghao-wu/CRF-AE,N
efficient contextualized representation: language model pruning for sequence labeling,https://github.com/LiyuanLucasLiu/LD-Net,N
semi-supervised sequence tagging with bidirectional language models,https://github.com/gaohuan2015/NLPTool,Y
neural architectures for named entity recognition,https://github.com/clab/stack-lstm-ner,N
neural architectures for named entity recognition,https://github.com/neeleshkshukla/Entity-Role-Detection-Paper,Y
neural architectures for named entity recognition,https://github.com/brendanxwhitaker/tagger-v,Y
neural architectures for named entity recognition,https://github.com/karlstratos/mention2vec,Y
neural architectures for named entity recognition,https://github.com/glample/tagger,Y
neural architectures for named entity recognition,https://github.com/marekrei/sequence-labeler,Y
neural architectures for named entity recognition,https://github.com/zysite/post,Y
neural architectures for named entity recognition,https://github.com/IsabelMeraner/BotanicalNER,Y
neural architectures for named entity recognition,https://github.com/JZ-LIANG/CRF-LSTM-NER,Y
neural architectures for named entity recognition,https://github.com/IBM/MAX-Named-Entity-Tagger,Y
neural architectures for named entity recognition,https://github.com/zalandoresearch/flair,Y
neural architectures for named entity recognition,https://github.com/SaeedNajafi/ac-tagger,Y
neural architectures for named entity recognition,https://github.com/guillaumegenthial/sequence_tagging,Y
neural architectures for named entity recognition,https://github.com/devkotasabin/EBM-NLP,Y
neural architectures for named entity recognition,https://github.com/nvtienanh/NER,Y
neural architectures for named entity recognition,https://github.com/yyHaker/NamedEntityRecognition,Y
neural architectures for named entity recognition,https://github.com/gitzgk/nlp-beginner,Y
neural architectures for named entity recognition,https://github.com/Hironsan/anago,Y
neural architectures for named entity recognition,https://github.com/deepmipt/ner,Y
neural architectures for named entity recognition,https://github.com/deepmipt/DeepPavlov,N
variational sequential labelers for semi-supervised learning,https://github.com/mingdachen/vsl,Y
"multi-task identification of entities, relations, and coreference for scientific knowledge graph construction",https://github.com/luanyi/DyGIE,N
reside: improving distantly-supervised neural relation extraction using side information,https://github.com/malllabiisc/RESIDE,N
neural relation extraction with selective attention over instances,https://github.com/thunlp/NRE,N
adaptively connected neural networks,https://github.com/lxtGH/OctaveConv_pytorch,Y
adaptively connected neural networks,https://github.com/wanggrun/Adaptively-Connected-Neural-Networks,N
large-scale learnable graph convolutional networks,https://github.com/divelab/lgcn,Y
graph attention networks,https://github.com/HeapHop30/graph-attention-nets,Y
graph attention networks,https://github.com/stellargraph/stellargraph,Y
graph attention networks,https://github.com/PetarV-/GAT,Y
graph attention networks,https://github.com/mitya8128/experiments_notes,Y
graph attention networks,https://github.com/danielegrattarola/keras-gat,Y
graph attention networks,https://github.com/ds4dm/sGat,Y
graph attention networks,https://github.com/handasontam/GAT-with-edgewise-attention,Y
graph attention networks,https://github.com/snownus/COOP,Y
graph attention networks,https://github.com/YunseobShin/wiki_GAT,N
graph attention networks,https://github.com/ColdenChan/GAT_D,N
graph attention networks,https://github.com/Diego999/pyGAT,Y
graph attention networks,https://github.com/ds4dm/sparse-gcn,Y
graph attention networks,https://github.com/weiyangfb/PyTorchSparseGAT,Y
geometric deep learning on graphs and manifolds using mixture model cnns,https://github.com/HeapHop30/graph-attention-nets,Y
semi-supervised classification with graph convolutional networks,https://github.com/yuehanlyu/pygcn_NGCN,N
semi-supervised classification with graph convolutional networks,https://github.com/adalld/kdd2019,Y
semi-supervised classification with graph convolutional networks,https://github.com/yangji9181/RELEARN,N
semi-supervised classification with graph convolutional networks,https://github.com/tkipf/gcn,Y
semi-supervised classification with graph convolutional networks,https://github.com/stellargraph/stellargraph,N
semi-supervised classification with graph convolutional networks,https://github.com/tkipf/ica,Y
semi-supervised classification with graph convolutional networks,https://github.com/VoVAllen/diffpool,N
semi-supervised classification with graph convolutional networks,https://github.com/thibaudmartinez/weighted-gnn,N
semi-supervised classification with graph convolutional networks,https://github.com/tkipf/pygcn,Y
semi-supervised classification with graph convolutional networks,https://github.com/sufeidechabei/graphnets_gcn,Y
semi-supervised classification with graph convolutional networks,https://github.com/YijingZhang/GCN,Y
semi-supervised classification with graph convolutional networks,https://github.com/liuzhencheng/GCN_CODE,Y
semi-supervised classification with graph convolutional networks,https://github.com/JHart96/keras_gcn_sequence_labelling,Y
semi-supervised classification with graph convolutional networks,https://github.com/mcbaron/fluxGCN,Y
semi-supervised classification with graph convolutional networks,https://github.com/luohongyin/FG-GCN,Y
semi-supervised classification with graph convolutional networks,https://github.com/JHart96/keras-gcn-sequence-labelling,Y
semi-supervised classification with graph convolutional networks,https://github.com/tkipf/gae,Y
semi-supervised classification with graph convolutional networks,https://github.com/Microsoft/gated-graph-neural-network-samples,Y
semi-supervised classification with graph convolutional networks,https://github.com/ltecot/gcn_robustness,N
semi-supervised classification with graph convolutional networks,https://github.com/BrizziB/Graph-Classification-with-GCN,Y
semi-supervised classification with graph convolutional networks,https://github.com/HoganZhang/pygcn_python3,Y
revisiting semi-supervised learning with graph embeddings,https://github.com/tkipf/ica,Y
revisiting semi-supervised learning with graph embeddings,https://github.com/tkipf/gcn,Y
revisiting semi-supervised learning with graph embeddings,https://github.com/kimiyoung/planetoid,N
revisiting semi-supervised learning with graph embeddings,https://github.com/firojalam/domain-adaptation,N
deepwalk: online learning of social representations,https://github.com/leihuayi/NetworkEmbedding,N
deepwalk: online learning of social representations,https://github.com/williamleif/GraphSAGE,Y
deepwalk: online learning of social representations,https://github.com/shenweichen/GraphEmbedding,N
hdltex: hierarchical deep learning for text classification,https://github.com/kk7nc/HDLTex,N
hierarchical neural networks for sequential sentence classification in medical scientific abstracts,https://github.com/jind11/HSLN-Joint-Sentence-Classification,N
structural scaffolds for citation intent classification in scientific publications,https://github.com/allenai/scicite,N
measuring the evolution of a scientific field through citation frames,https://github.com/davidjurgens/citation-function,N
revisiting lstm networks for semi-supervised text classification via mixed objective function,https://github.com/DevSinghSachan/ssl_text_classification,N
very deep convolutional networks for text classification,https://github.com/nuke705/VDCNN,Y
very deep convolutional networks for text classification,https://github.com/mukesh-mehta/VDCNN,Y
very deep convolutional networks for text classification,https://github.com/vaibhavska/Very-Deep-Convolutional-Neural-Network-for-Sentiment-Analysis,Y
very deep convolutional networks for text classification,https://github.com/Johnnylyu/Text-Mining-and-Classification-for-Attain,Y
very deep convolutional networks for text classification,https://github.com/opringle/VDCNN-for-text-classification,Y
very deep convolutional networks for text classification,https://github.com/nithishkaviyan/Sentiment-Analysis-of-Yelp-Reviews,Y
very deep convolutional networks for text classification,https://github.com/threelittlemonkeys/vdcnn-pytorch,Y
very deep convolutional networks for text classification,https://github.com/polkadottedwalrus/Fanfiction,Y
very deep convolutional networks for text classification,https://github.com/nuke705/DeepLearning,Y
very deep convolutional networks for text classification,https://github.com/zonetrooper32/VDCNN,Y
very deep convolutional networks for text classification,https://github.com/tdiggelm/nn-experiments,Y
very deep convolutional networks for text classification,https://github.com/phanthaithuc/deep-cnn,Y
very deep convolutional networks for text classification,https://github.com/yeahshow/word2vec_medical_record,Y
abstractive text classification using sequence-to-convolution neural networks,https://github.com/tgisaturday/Seq2CNN,N
translations as additional contexts for sentence classification,https://github.com/rktamplayo/MCFA,Y
a large self-annotated corpus for sarcasm,https://github.com/NLPrinceton/SARC,N
cascade: contextual sarcasm detection in online discussion forums,https://github.com/SenticNet/CASCADE--ContextuAl-SarCAsm-DEtector,Y
shape robust text detection with progressive scale expansion network,https://github.com/whai362/PSENet,Y
detecting curve text in the wild: new dataset and new solution,https://github.com/Yuliang-Liu/Curve-Text-Detector,N
east: an efficient and accurate scene text detector,https://github.com/kurapan/EAST,Y
east: an efficient and accurate scene text detector,https://github.com/liushuchun/EAST.pytorch,Y
east: an efficient and accurate scene text detector,https://github.com/zxytim/EAST,Y
east: an efficient and accurate scene text detector,https://github.com/argman/EAST,Y
east: an efficient and accurate scene text detector,https://github.com/pengyuanzhuo/EAST-Pytorch,N
east: an efficient and accurate scene text detector,https://github.com/isaacaddis/Seung,Y
east: an efficient and accurate scene text detector,https://github.com/ZXdatascience/TextDetectorAndroid,Y
east: an efficient and accurate scene text detector,https://github.com/Greeser/holyvk,Y
detecting oriented text in natural images by linking segments,https://github.com/GuoLiuFang/seglink-lfs,Y
detecting oriented text in natural images by linking segments,https://github.com/curbmap/curbmap-ml,Y
detecting oriented text in natural images by linking segments,https://github.com/bgshih/seglink,Y
detecting oriented text in natural images by linking segments,https://github.com/Yiming992/Test-Linking-Segments-Text-Localization,Y
detecting oriented text in natural images by linking segments,https://github.com/YohannaYin/segmentlink_yh,Y
fots: fast oriented text spotting with a unified network,https://github.com/xieyufei1993/FOTS,N
r2cnn: rotational region cnn for orientation robust scene text detection,https://github.com/dafanghe/Tensorflow_SceneText_Oriented_Box_Predictor,Y
single shot text detector with regional attention,https://github.com/BestSonny/SSTD,Y
learning latent dynamics for planning from pixels,https://github.com/google-research/planet,Y
the neural hype and comparisons against weak baselines,https://github.com/castorini/Anserini,N
from neural re-ranking to neural ranking: learning a sparse representation for inverted indexing,https://github.com/hamed-zamani/snrm,N
nprf: a neural pseudo relevance feedback framework for ad-hoc information retrieval,https://github.com/ucasir/NPRF,Y
neural ranking models with weak supervision,https://github.com/mikvrax/TrecingLab,N
a deep relevance matching model for ad-hoc retrieval,https://github.com/faneshion/DRMM,N
a deep relevance matching model for ad-hoc retrieval,https://github.com/sebastian-hofstaetter/neural-ranking-drmm,N
deep relevance ranking using enhanced document-query interactions,https://github.com/nlpaueb/deep-relevance-ranking,N
large-scale image retrieval with attentive deep local features,https://github.com/tensorflow/models/tree/master/research/delf,Y
large-scale image retrieval with attentive deep local features,https://github.com/jandaldrop/landmark-recognition-challenge,Y
large-scale image retrieval with attentive deep local features,https://github.com/pandigreat/DELF,Y
large-scale image retrieval with attentive deep local features,https://github.com/nashory/DeLF-pytorch,Y
deep image retrieval: learning global representations for image search,https://github.com/talal579/Deep-image-matching,Y
cnn image retrieval learns from bow: unsupervised fine-tuning with hard examples,https://github.com/filipradenovic/cnnimageretrieval,N
cnn image retrieval learns from bow: unsupervised fine-tuning with hard examples,https://github.com/RuibinMa/comp755project-ruibinma,N
cnn image retrieval learns from bow: unsupervised fine-tuning with hard examples,https://github.com/filipradenovic/cnnimageretrieval-pytorch,N
retrieving similar e-commerce images using deep learning,https://github.com/gofynd/mildnet,Y
mildnet: a lightweight single scaled deep ranking architecture,https://github.com/gofynd/mildnet,Y
multigrain: a unified image embedding for classes and instances,https://github.com/facebookresearch/multigrain,Y
unsupervised domain adaptation: an adaptive feature norm approach,https://github.com/jihanyang/AFN,N
visual domain adaptation with manifold embedded distribution alignment,https://github.com/jindongwang/transferlearning,N
looking back at labels: a class based domain adaptation technique,https://github.com/vinodkkurmi/DiscriminatorDomainAdaptation,Y
proxylessnas: direct neural architecture search on target task and hardware,https://github.com/songhan/SqueezeNet-Residual,N
proxylessnas: direct neural architecture search on target task and hardware,https://github.com/songhan/DSD,N
proxylessnas: direct neural architecture search on target task and hardware,https://github.com/osmr/imgclsmob,N
proxylessnas: direct neural architecture search on target task and hardware,https://github.com/MIT-HAN-LAB/ProxylessNAS,N
regularized evolution for image classifier architecture search,https://github.com/CiscoAI/amla,Y
path-level network transformation for efficient architecture search,https://github.com/han-cai/PathLevel-EAS,Y
learning efficient convolutional networks through network slimming,https://github.com/liuzhuang13/slimming,N
picture it in your mind: generating high level visual representations from textual descriptions,https://github.com/AlexMoreo/tensorflow-Text2Vis,N
a corpus for multilingual document classification in eight languages,https://github.com/facebookresearch/MLDoc,N
pi-rec: progressive image reconstruction network with edge and color domain,https://github.com/youyuge34/PI-REC,N
graph convolutional matrix completion,https://github.com/tkipf/gae,Y
graph convolutional matrix completion,https://github.com/riannevdberg/gc-mc,N
graph convolutional matrix completion,https://github.com/RyanLu32/GCMC,N
hybrid recommender system based on autoencoders,https://github.com/jowoojun/collaborative_filtering_keras,N
hybrid recommender system based on autoencoders,https://github.com/fstrub95/Autoencoders_cf,N
neural network matrix factorization,https://github.com/jstol/neural-net-matrix-factorization,N
dictionary learning for massive matrix factorization,https://github.com/arthurmensch/modl,N
variational autoencoders for collaborative filtering,https://github.com/dawenl/vae_cf,Y
variational autoencoders for collaborative filtering,https://github.com/amoussawi/recoder,N
deep models of interactions across sets,https://github.com/jhartford/AutoEncSets,N
deep models of interactions across sets,https://github.com/mravanba/deep_exchangeable_tensors,N
geometric matrix completion with recurrent multi-graph neural networks,https://github.com/fmonti/mgcnn,Y
training deep autoencoders for collaborative filtering,https://github.com/marlesson/recsys_autoencoders,N
training deep autoencoders for collaborative filtering,https://github.com/NVIDIA/DeepRecommender,N
training deep autoencoders for collaborative filtering,https://github.com/suvigyavijay/DeepRecommender,N
training deep autoencoders for collaborative filtering,https://github.com/yrbahn/Deep-AutoEncoders-for-Collaborative-Filtering,N
training deep autoencoders for collaborative filtering,https://github.com/butroy/movie-autoencoder,N
matrix completion on graphs,https://github.com/HarishFulara07/MCGraphs-scRNAseq,N
end-to-end neural coreference resolution,https://github.com/Achint08/e2e-coref-keras,Y
end-to-end neural coreference resolution,https://github.com/kentonl/e2e-coref,N
end-to-end neural coreference resolution,https://github.com/shayneobrien/coreference-resolution,Y
deep joint entity disambiguation with local neural attention,https://github.com/dalab/deep-ed,Y
"models matter, so does training: an empirical study of cnns for optical flow estimation",https://github.com/NVlabs/PWC-Net,Y
liteflownet: a lightweight convolutional neural network for optical flow estimation,https://github.com/twhui/LiteFlowNet,N
optical flow estimation using a spatial pyramid network,https://github.com/anuragranj/spynet,Y
optical flow estimation using a spatial pyramid network,https://github.com/rickyHong/tfoptflow-repl,Y
optical flow estimation using a spatial pyramid network,https://github.com/guanfuchen/video_obj,Y
optical flow estimation using a spatial pyramid network,https://github.com/philferriere/tfoptflow,Y
pointrcnn: 3d object proposal generation and detection from point cloud,https://github.com/sshaoshuai/PointRCNN,N
joint 3d proposal generation and object detection from view aggregation,https://github.com/kujason/ip_basic,N
joint 3d proposal generation and object detection from view aggregation,https://github.com/kujason/avod,Y
joint 3d proposal generation and object detection from view aggregation,https://github.com/asharakeh/kitti_native_evaluation,N
roarnet: a robust 3d object detection based on region approximation refinement,https://github.com/Kiwoo/RoarNet,N
pointpillars: fast encoders for object detection from point clouds,https://github.com/nutonomy/second.pytorch,Y
yolov3: an incremental improvement,https://github.com/thecryboy/DECONV,Y
yolov3: an incremental improvement,https://github.com/DarkGeekMS/Pytorch-YOLOv3-Implementation,Y
yolov3: an incremental improvement,https://github.com/guagen/yolov3-ultralytics-source,Y
yolov3: an incremental improvement,https://github.com/lmeulen/PeopleCounter,N
yolov3: an incremental improvement,https://github.com/fofore/yolov3-pyroch,Y
yolov3: an incremental improvement,https://github.com/ultralytics/yolov3,Y
yolov3: an incremental improvement,https://github.com/tanimutomo/ganonymizer,Y
yolov3: an incremental improvement,https://github.com/Stick-To/YOLO-tensorflow,Y
yolov3: an incremental improvement,https://github.com/GimSungwoo/Two-view-3D-construction,Y
yolov3: an incremental improvement,https://github.com/thecryboy/yolov3_ultralytics_deconv,Y
yolov3: an incremental improvement,https://github.com/Kev1nZheng/yolov_mask,Y
yolov3: an incremental improvement,https://github.com/zhangming8/pytorch-yolov3,N
yolov3: an incremental improvement,https://github.com/xiao110xy/water_detection,Y
yolov3: an incremental improvement,https://github.com/linghu8812/Data_Distillation,Y
yolov3: an incremental improvement,https://github.com/Rainton/handtrain,Y
yolov3: an incremental improvement,https://github.com/BlackAngel1111/Yolo3_Pytorch,Y
yolov3: an incremental improvement,https://github.com/Stick-To/YOLO-TF,Y
yolov3: an incremental improvement,https://github.com/benjaminrwilson/yolov3,Y
"yolo9000: better, faster, stronger",https://github.com/williamccondori/darknet-alexey,N
"yolo9000: better, faster, stronger",https://github.com/dc17540/darknet,N
"yolo9000: better, faster, stronger",https://github.com/soccergame/darknet,N
"yolo9000: better, faster, stronger",https://github.com/jianing-sun/Mask-YOLO,N
"yolo9000: better, faster, stronger",https://github.com/Geeks-Sid/Information-on-deep-learning,Y
"yolo9000: better, faster, stronger",https://github.com/yytang2012/darknet,N
"yolo9000: better, faster, stronger",https://github.com/gpandu/Object-detection,N
"yolo9000: better, faster, stronger",https://github.com/wpsliu123/yolo-windows,N
"yolo9000: better, faster, stronger",https://github.com/santhisenan/car-detection-using-yolov2,Y
"yolo9000: better, faster, stronger",https://github.com/EMsnap/RobotSorting,N
"yolo9000: better, faster, stronger",https://github.com/tianhai123/yolov3,N
"yolo9000: better, faster, stronger",https://github.com/li-yibing/oceannet,N
"yolo9000: better, faster, stronger",https://github.com/khaled2ahmed/k2a,N
"yolo9000: better, faster, stronger",https://github.com/vuongtrannguyenkhoi/darknet,N
"yolo9000: better, faster, stronger",https://github.com/edward2306/dark,N
"yolo9000: better, faster, stronger",https://github.com/darshans0200/YOLOTest,N
"yolo9000: better, faster, stronger",https://github.com/Stick-To/YOLO-TF,Y
"yolo9000: better, faster, stronger",https://github.com/keshav47/Face-Recognition-And-Verification,Y
"yolo9000: better, faster, stronger",https://github.com/maxpastor/Tensorflow_Juin,Y
"yolo9000: better, faster, stronger",https://github.com/zj463261929/darknet_mAP,N
"yolo9000: better, faster, stronger",https://github.com/kyunghwan/darknet_v3,N
"yolo9000: better, faster, stronger",https://github.com/Sushma07/dancedarknet,N
"yolo9000: better, faster, stronger",https://github.com/SteveBetter/darknet_ac,N
"yolo9000: better, faster, stronger",https://github.com/slopezRedfox/CoffeeCoffee,N
"yolo9000: better, faster, stronger",https://github.com/williamccondori/YOLO-NFPA,N
"yolo9000: better, faster, stronger",https://github.com/kke0217/darknet_WIN,N
"yolo9000: better, faster, stronger",https://github.com/vantupham/darknet,N
"yolo9000: better, faster, stronger",https://github.com/deepakHonakeri05/darknet,N
"yolo9000: better, faster, stronger",https://github.com/zhuqiang00099/abyolov3,N
"yolo9000: better, faster, stronger",https://github.com/buptdbj/darknet-windows-linux,N
"yolo9000: better, faster, stronger",https://github.com/Stick-To/YOLO-tensorflow,Y
"yolo9000: better, faster, stronger",https://github.com/vinliao/object-detection-python,Y
"yolo9000: better, faster, stronger",https://github.com/ShaharrHorn/Detection-Game-logic,N
"yolo9000: better, faster, stronger",https://github.com/yuanxy92/darknet,Y
"yolo9000: better, faster, stronger",https://github.com/ryubidragonfire/mydarknet,N
"yolo9000: better, faster, stronger",https://github.com/RobbertBrand/Yolo-Tensorflow-Implementation,Y
"yolo9000: better, faster, stronger",https://github.com/manankshastri/Object-Detection,Y
"yolo9000: better, faster, stronger",https://github.com/bharatsush/object_detection,N
"yolo9000: better, faster, stronger",https://github.com/CosmiQ/yolt,Y
"yolo9000: better, faster, stronger",https://github.com/hemp110/darknet-myb,N
"yolo9000: better, faster, stronger",https://github.com/dragon20002/STLC_Server,N
"yolo9000: better, faster, stronger",https://github.com/avanetten/yolt,Y
"yolo9000: better, faster, stronger",https://github.com/anookeen/yolo,N
"yolo9000: better, faster, stronger",https://github.com/KingBoyBIT/yolov3test,N
"yolo9000: better, faster, stronger",https://github.com/kirilcvetkov92/Vehicle-Detection,Y
"yolo9000: better, faster, stronger",https://github.com/alcaster/Binary-yolo,Y
"yolo9000: better, faster, stronger",https://github.com/Cadmium-CD/socFinal,N
"yolo9000: better, faster, stronger",https://github.com/zhreshold/mxnet-yolo,Y
"yolo9000: better, faster, stronger",https://github.com/teemoeric/projet,N
"yolo9000: better, faster, stronger",https://github.com/saednasir/Helmet_Yolo,N
"yolo9000: better, faster, stronger",https://github.com/saber2011/darknet,N
"yolo9000: better, faster, stronger",https://github.com/toufiksk/darknet,N
"yolo9000: better, faster, stronger",https://github.com/yudie433/darknet,N
"yolo9000: better, faster, stronger",https://github.com/hankpark0706/darknet,N
"yolo9000: better, faster, stronger",https://github.com/manishravula/yolov3_tiny_pruned,N
"yolo9000: better, faster, stronger",https://github.com/saurabh241930/yolo_v2_tensorflow,Y
"yolo9000: better, faster, stronger",https://github.com/DonCorle0ne/damage_detection,N
"yolo9000: better, faster, stronger",https://github.com/ghaniskn/GCorp-Darknet,N
"yolo9000: better, faster, stronger",https://github.com/rnirdhar/yoloMultiClass,N
"yolo9000: better, faster, stronger",https://github.com/gpandu/YOLO2_ObjectDetection,N
"yolo9000: better, faster, stronger",https://github.com/eric-erki/darknet,N
"yolo9000: better, faster, stronger",https://github.com/mathieuorhan/darknet,N
"yolo9000: better, faster, stronger",https://github.com/hallditq/Darknet,N
"yolo9000: better, faster, stronger",https://github.com/3epochs/you-only-look-once,Y
"yolo9000: better, faster, stronger",https://github.com/thtrieu/darkflow,Y
"yolo9000: better, faster, stronger",https://github.com/longcw/yolo2-pytorch,Y
"yolo9000: better, faster, stronger",https://github.com/Ereebay/Deep-Learning-Documents,N
"yolo9000: better, faster, stronger",https://github.com/ermuur/darknet,N
"yolo9000: better, faster, stronger",https://github.com/Ereebay/DeepLearningDocuments,N
"yolo9000: better, faster, stronger",https://github.com/eric-erki/yolov3,N
"yolo9000: better, faster, stronger",https://github.com/zhangxiawen/darknet-master,Y
"yolo9000: better, faster, stronger",https://github.com/FMsunyh/keras-yolo2,Y
"yolo9000: better, faster, stronger",https://github.com/kwon852456/darkent,N
"yolo9000: better, faster, stronger",https://github.com/bobby20180331/darknet_pycharm,N
"yolo9000: better, faster, stronger",https://github.com/dwaithe/yolov2,N
"yolo9000: better, faster, stronger",https://github.com/PolarisAlpha/darknet,N
"yolo9000: better, faster, stronger",https://github.com/aminekha/AI-For-2022,N
"yolo9000: better, faster, stronger",https://github.com/rnirdhar/yoloTestOneClass,N
"yolo9000: better, faster, stronger",https://github.com/trongnghia00/darknet,N
"yolo9000: better, faster, stronger",https://github.com/TheHidden1/YOLO.JS,N
cornernet-lite: efficient keypoint based object detection,https://github.com/princeton-vl/CornerNet-Lite,Y
"you only look once: unified, real-time object detection",https://github.com/yeahkun/caffe-yolo,Y
"you only look once: unified, real-time object detection",https://github.com/Geeks-Sid/Information-on-deep-learning,Y
"you only look once: unified, real-time object detection",https://github.com/ZUCCBBQ/yoloV3-,Y
"you only look once: unified, real-time object detection",https://github.com/SHU-FLYMAN/Yolov1-TensorFlow,Y
"you only look once: unified, real-time object detection",https://github.com/jiama843/tf-mnist,Y
"you only look once: unified, real-time object detection",https://github.com/Vonski/wdi19,Y
"you only look once: unified, real-time object detection",https://github.com/roya90/android-yolo,Y
"you only look once: unified, real-time object detection",https://github.com/Stick-To/YOLO-TF,N
"you only look once: unified, real-time object detection",https://github.com/ssaru/You_Only_Look_Once,Y
"you only look once: unified, real-time object detection",https://github.com/Banus/caffe-demo,Y
"you only look once: unified, real-time object detection",https://github.com/keshav47/Face-Recognition-And-Verification,Y
"you only look once: unified, real-time object detection",https://github.com/eric-erki/android-yolo,Y
"you only look once: unified, real-time object detection",https://github.com/noelcodes/YOLO,Y
"you only look once: unified, real-time object detection",https://github.com/chuongngd/Images-Object-Detection,Y
"you only look once: unified, real-time object detection",https://github.com/Stick-To/YOLO-tensorflow,N
"you only look once: unified, real-time object detection",https://github.com/metu-kovan/METU-ALET,Y
"you only look once: unified, real-time object detection",https://github.com/WaelOuni/MergeTenserFlowWithOdb,Y
"you only look once: unified, real-time object detection",https://github.com/makatx/YOLO_ResNet,Y
"you only look once: unified, real-time object detection",https://github.com/ryol8888/Yolo_v2,Y
"you only look once: unified, real-time object detection",https://github.com/RobbertBrand/Yolo-Tensorflow-Implementation,Y
"you only look once: unified, real-time object detection",https://github.com/manankshastri/Object-Detection,Y
"you only look once: unified, real-time object detection",https://github.com/dongkyuk0419/YOLO,Y
"you only look once: unified, real-time object detection",https://github.com/elias-ramzi/svhn_classifier,N
"you only look once: unified, real-time object detection",https://github.com/kirilcvetkov92/Vehicle-Detection,Y
"you only look once: unified, real-time object detection",https://github.com/jiama843/tf-YOLO-pascalVOC,Y
"you only look once: unified, real-time object detection",https://github.com/natanielruiz/android-yolo,Y
"you only look once: unified, real-time object detection",https://github.com/you-leee/deep-nn-examples,Y
"you only look once: unified, real-time object detection",https://github.com/sprenkle/VectorCards,Y
"you only look once: unified, real-time object detection",https://github.com/elias-ramzi/number-pod,N
"you only look once: unified, real-time object detection",https://github.com/subodh-malgonde/vehicle-detection,Y
"you only look once: unified, real-time object detection",https://github.com/saurabh241930/yolo_v2_tensorflow,Y
"you only look once: unified, real-time object detection",https://github.com/BathVisArtData/PeopleArt,Y
"you only look once: unified, real-time object detection",https://github.com/sebsquire/Automated-Video-Filtering-YOLOv2,Y
"you only look once: unified, real-time object detection",https://github.com/3epochs/you-only-look-once,Y
"you only look once: unified, real-time object detection",https://github.com/thtrieu/darkflow,Y
"you only look once: unified, real-time object detection",https://github.com/Ereebay/Deep-Learning-Documents,N
"you only look once: unified, real-time object detection",https://github.com/Ereebay/DeepLearningDocuments,N
"you only look once: unified, real-time object detection",https://github.com/abajaj945/Ship-Detection-using-Tensorflow,Y
"you only look once: unified, real-time object detection",https://github.com/Guanghan/ROLO,Y
blitznet: a real-time deep network for scene understanding,https://github.com/dvornikita/blitznet,Y
blitznet: a real-time deep network for scene understanding,https://github.com/ShunyuYao/blitznet_instance_segment,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/zdaxie/DiDi,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/qilei123/fpn_crop_v2_4d,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/qilei123/fpn_crop,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/Code-0x00/caffe_windows,N
r-fcn: object detection via region-based fully convolutional networks,https://github.com/qilei123/DeformableConvV2,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/makefile/frcnn,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/MonsterPeng/Deformable-ConvNets-master,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/NVIDIAAICITYCHALLENGE/AICity_Team6_ISU,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/xdever/RFCN-tensorflow,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/guanfuchen/Deformable-ConvNets,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/fourmi1995/IronExperiment-DCN,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/qilei123/DEEPLAB_4_RETINA,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/daijifeng001/r-fcn,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/stupidZZ/pyc_repo,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/zzdxfei/defor_conv_mxnet_code,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/qilei123/DeformableConvV2_crop,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/princewang1994/RFCN_CoupleNet.pytorch,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/chenghuaiyu/caffe,N
r-fcn: object detection via region-based fully convolutional networks,https://github.com/qilei123/sod_v1_demo,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/Xushanbo/caffe-master,N
r-fcn: object detection via region-based fully convolutional networks,https://github.com/zengzhaoyang/trident,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/qilei123/fpn_crop_v1_5d,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/busyboxs/Some-resources-useful-for-me,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/wincelinux/ai-dn-FasterRCNN-caffe,N
r-fcn: object detection via region-based fully convolutional networks,https://github.com/zengzhaoyang/Weak_Detection,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/msracver/Deformable-ConvNets,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/Feynman27/pytorch-detect-rfcn,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/ghamarian/rfcn,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/facebookresearch/detectron,N
r-fcn: object detection via region-based fully convolutional networks,https://github.com/xiaoyongzhu/Deformable-ConvNets,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/qilei123/DEEPLAB_4_RETINAIMG,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/chenbys/GuidedOffset,Y
r-fcn: object detection via region-based fully convolutional networks,https://github.com/qilei123/sod_v1,Y
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/xinpingwang/tf-faster-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/1512159/tf-faster-rcnn-medico,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/leonardhan1979/fasterRCNN,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/nautilus261/tf-faster-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/lukewenMX/RCNN_simple_detection,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/yanxp/rcnn-discovery,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/ithuanhuan/gpu-py3-faster-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/dafanghe/Tensorflow_SceneText_Oriented_Box_Predictor,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/Kenneth-Wong/tf-faster-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/charlesYangM/py-faster-rcnn-80.28,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/guanfuchen/py-faster-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/ShaoqingRen/faster_rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/neuqgz/modify-faster-rcnn-tf,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/phtruongan/py-faster-rcnn-docker,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/cantaible/tf-faster-rcnn-note,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/clemkoa/faster-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/sonnguyen64/horus-tf-faster-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/noelcodes/cloth-recognition-version2,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/xzabg/faster-rcnn-with-Caltech,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/Bigwode/rebar-detection-competition,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/salman-ghauri/keras_frcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/makefile/frcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/tensorpack/tensorpack,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/xzgz/faster-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/zhoujqhappy/simpledet_int8,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/mengyingfei/faster-rcnn-tf,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/PengchengAi/tf-faster-rcnn-pcai,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/zly9844/py-faster-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/qilei123/pyfasterrcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/pransen/faster_rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/cftang0827/pedestrian_detection_ssdlite,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/soulguy/Faster-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/WalterMa/gluon-faster-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/bareblackfoot/lddp-tf-faster-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/anlimo1510/faster-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/playerkk/face-py-faster-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/smallcorgi/Faster-RCNN_TF,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/busyboxs/faster_rcnn_voc,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/jkulhanek/faster-rcnn-pytorch,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/zxqcreations/faster-rcnn-tf,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/Arthur-Shi/py-faster-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/mayankagarwal44442/Object-Detection-and-localization,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/huan123/py-fatser-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/rickyHong/faster-rcnn-tensorflow-repl,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/UPCLJ/py-faster-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/tusimple/simpledet,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/zacks417/faster-rcnn-tf,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/yfnn/fused-model,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/chenyuntc/simple-faster-rcnn-pytorch,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/jooyounghun/AI-Team-5,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/cantaible/tf-faster-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/csmiler/tf-faster-rcnn-cpu,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/sunqiangxtcsun/faster-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/ishanashastri/FRCNN-for-breast-tumor-detection,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/rbgirshick/py-faster-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/insigh/Faster-RCNN-Tensorflow,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/AkashGanesan/PedestrianAttention,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/endernewton/tf-faster-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/godspeedcurry/lung-nodule-detection,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/jiggles546/60499_Project,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/yfnn/person_subclass,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/chcyj/my-project,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/daxiapazi/faster-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/ChestnutLi/tf-faster-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/yzgrfsy/tf-fastrcnn-crop,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/leochangzliao/OPBM,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/rickyHong/py-faster-rcnn-repl,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/Sunnyhoster/tf-faster-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/ruotianluo/pytorch-faster-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/saumya-jetley/pp_ICVSS15_TacklingBkgndDifferently,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/1512159/demo_frcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/lincaiming/py-faster-rcnn-update,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/evaristr/py-faster_rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/xunhen/py-faster-rcnn-wjc,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/metu-kovan/METU-ALET,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/clemkoa/fasterrcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/zhong110020/py-faster-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/spideralessio/coconutnut,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/rickyHong/py-faster-rcnn-repl-cudnn5-support,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/PythonZhengbinChen/tf-faster-rcnn-cpu,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/rickyHong/pytorch-faster-rcnn-repl,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/lincaiming/py-faster-rcnn-windows,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/facebookresearch/detectron,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/NNDam/RPN-from-scratch,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/ithuanhuan/py-fatser-rcnn,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/wayne1204/NOAA-fish-finding,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/kevinjliang/tf-Faster-RCNN,N
faster r-cnn: towards real-time object detection with region proposal networks,https://github.com/achiyaj/vqa-sandbox,N
pcl: proposal cluster learning for weakly supervised object detection,https://github.com/ppengtang/oicr,N
multiple instance detection network with online instance classifier refinement,https://github.com/ppengtang/oicr,N
contextlocnet: context-aware deep network models for weakly supervised localization,https://github.com/vadimkantorov/contextlocnet,N
weakly supervised deep detection networks,https://github.com/hbilen/WSDDN,Y
soft proposal networks for weakly supervised object localization,https://github.com/yeezhu/SPN.pytorch,N
soft proposal networks for weakly supervised object localization,https://github.com/ZhouYanzhao/SPN,N
recurrent pixel embedding for instance grouping,https://github.com/aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping,N
recurrent pixel embedding for instance grouping,https://github.com/aimerykong/predictive-filter-flow,N
semantic instance segmentation via deep metric learning,https://github.com/alicranck/instance-seg,N
finding a needle in the haystack: attention-based classification of high resolution microscopy images,https://github.com/BMIRDS/deepslide,N
detecting cancer metastases on gigapixel pathology images,https://github.com/olahosa/adl_cancer_detection,N
detecting cancer metastases on gigapixel pathology images,https://github.com/Srinidhi-kv/Cancer_metastasis_detection,N
detecting cancer metastases on gigapixel pathology images,https://github.com/martin-fabbri/kaggle-histopathologic-cancer-detector,N
detecting cancer metastases on gigapixel pathology images,https://github.com/Reemr/Cancer-detection,N
flow-guided feature aggregation for video object detection,https://github.com/msracver/Flow-Guided-Feature-Aggregation,N
precise detection in densely packed scenes,https://github.com/eg4000/SKU110K_CVPR19,N
deepflux for skeletons in the wild,https://github.com/YukangWang/DeepFlux,N
one-shot instance segmentation,https://github.com/bethgelab/siamese-mask-rcnn,Y
scale-aware trident networks for object detection,https://github.com/zhoujqhappy/simpledet_int8,N
scale-aware trident networks for object detection,https://github.com/tusimple/simpledet,N
path aggregation network for instance segmentation,https://github.com/YuefeiZ/PANet,Y
path aggregation network for instance segmentation,https://github.com/ShuLiu1993/PANet,Y
centernet: keypoint triplets for object detection,https://github.com/Duankaiwen/CenterNet,Y
sniper: efficient multi-scale training,https://github.com/starimpact/arm_SNIPER,Y
sniper: efficient multi-scale training,https://github.com/MahyarNajibi/SNIPER,Y
"deformable convnets v2: more deformable, better results",https://github.com/qilei123/sod_v1_demo,Y
"deformable convnets v2: more deformable, better results",https://github.com/zengzhaoyang/trident,Y
"deformable convnets v2: more deformable, better results",https://github.com/qilei123/fpn_crop_v2_4d,Y
"deformable convnets v2: more deformable, better results",https://github.com/qilei123/fpn_crop_v1_5d,Y
"deformable convnets v2: more deformable, better results",https://github.com/qilei123/fpn_crop,Y
"deformable convnets v2: more deformable, better results",https://github.com/zzdxfei/defor_conv_mxnet_code,Y
"deformable convnets v2: more deformable, better results",https://github.com/msracver/Deformable-ConvNets,Y
"deformable convnets v2: more deformable, better results",https://github.com/qilei123/DeformableConvV2_crop,Y
"deformable convnets v2: more deformable, better results",https://github.com/qilei123/sod_v1,Y
"deformable convnets v2: more deformable, better results",https://github.com/qilei123/DeformableConvV2,Y
dsod: learning deeply supervised object detectors from scratch,https://github.com/szq0214/DSOD,N
objects as points,https://github.com/xingyizhou/CenterNet,Y
non-local neural networks,https://github.com/facebookresearch/detectron,N
non-local neural networks,https://github.com/facebookresearch/video-nonlocal-net,Y
non-local neural networks,https://github.com/changliu816/CV-paper-review,Y
non-local neural networks,https://github.com/huyz1117/Non_Local_Net_TensorFlow,N
non-local neural networks,https://github.com/owena11/papers,Y
non-local neural networks,https://github.com/MIT-HAN-LAB/temporal-shift-module,Y
m2det: a single-shot object detector based on multi-level feature pyramid network,https://github.com/qijiezhao/M2Det,Y
m2det: a single-shot object detector based on multi-level feature pyramid network,https://github.com/taashi-s/M2Det_keras,N
m2det: a single-shot object detector based on multi-level feature pyramid network,https://github.com/true-source/m2det-tf,N
m2det: a single-shot object detector based on multi-level feature pyramid network,https://github.com/cjpurackal/m2det-tf,N
bottom-up object detection by grouping extreme and center points,https://github.com/xingyizhou/ExtremeNet,Y
cascade r-cnn: delving into high quality object detection,https://github.com/makefile/frcnn,Y
cascade r-cnn: delving into high quality object detection,https://github.com/tensorpack/tensorpack,N
cascade r-cnn: delving into high quality object detection,https://github.com/tkuanlun350/Kaggle_Ship_Detection_2018,Y
cascade r-cnn: delving into high quality object detection,https://github.com/zhaoweicai/cascade-rcnn,N
retinamask: learning to predict masks improves state-of-the-art single-shot detection for free,https://github.com/leehsiu/maskrcnn_benchmark,N
retinamask: learning to predict masks improves state-of-the-art single-shot detection for free,https://github.com/EricKani/maskrcnnBenchmarkFromFackBook,N
retinamask: learning to predict masks improves state-of-the-art single-shot detection for free,https://github.com/squirrelim/sr-frcnn,N
retinamask: learning to predict masks improves state-of-the-art single-shot detection for free,https://github.com/bimal1988/detector,N
retinamask: learning to predict masks improves state-of-the-art single-shot detection for free,https://github.com/huaifeng1993/JinNanCompetition2,N
retinamask: learning to predict masks improves state-of-the-art single-shot detection for free,https://github.com/sarrrrry/maskrcnn-benchmark,N
retinamask: learning to predict masks improves state-of-the-art single-shot detection for free,https://github.com/howardyclo/maskrcnn-benchmark,N
retinamask: learning to predict masks improves state-of-the-art single-shot detection for free,https://github.com/bhfs9999/maskrcnn_isic,N
retinamask: learning to predict masks improves state-of-the-art single-shot detection for free,https://github.com/chengyangfu/retinamask,N
retinamask: learning to predict masks improves state-of-the-art single-shot detection for free,https://github.com/zsl1996/mask,N
retinamask: learning to predict masks improves state-of-the-art single-shot detection for free,https://github.com/qilei123/maskrcnn_benchmark_ROP,N
retinamask: learning to predict masks improves state-of-the-art single-shot detection for free,https://github.com/maxsenh/nuclei_cell_detect,N
retinamask: learning to predict masks improves state-of-the-art single-shot detection for free,https://github.com/meryusha/seeds_faster,N
retinamask: learning to predict masks improves state-of-the-art single-shot detection for free,https://github.com/wangtuo0820/maskrcnn-benchmark-expansion,N
retinamask: learning to predict masks improves state-of-the-art single-shot detection for free,https://github.com/ZhichengHuang/Food-Project,N
retinamask: learning to predict masks improves state-of-the-art single-shot detection for free,https://github.com/sadicLiu/mask_rcnn_code,N
group normalization,https://github.com/facebookresearch/detectron,N
group normalization,https://github.com/changliu816/CV-paper-review,Y
group normalization,https://github.com/ppwwyyxx/GroupNorm-reproduce,Y
group normalization,https://github.com/Jarvis73/Group_Normalization,Y
group normalization,https://github.com/blankWorld/GroupNorm-caffe,Y
group normalization,https://github.com/anzhao0503/group-normalization.pytorch,Y
group normalization,https://github.com/chengyangfu/pytorch-groupnormalization,Y
group normalization,https://github.com/vlievin/Unet,Y
group normalization,https://github.com/pren1/Group-Normalization-Example,Y
fcos: fully convolutional one-stage object detection,https://github.com/facebookresearch/maskrcnn-benchmark,Y
fcos: fully convolutional one-stage object detection,https://github.com/tianzhi0549/FCOS,Y
fcos: fully convolutional one-stage object detection,https://github.com/sarrrrry/maskrcnn-benchmark,Y
fcos: fully convolutional one-stage object detection,https://github.com/leehsiu/maskrcnn_benchmark,Y
cornernet: detecting objects as paired keypoints,https://github.com/w510056105/DeepLearning,Y
single-shot refinement neural network for object detection,https://github.com/kacerj3/caffe-refinedet,Y
single-shot refinement neural network for object detection,https://github.com/qilei123/RefineDet4OpenImagev4,Y
single-shot refinement neural network for object detection,https://github.com/laycoding/PostiveDet,Y
single-shot refinement neural network for object detection,https://github.com/sfzhang15/RefineDet,Y
single-shot refinement neural network for object detection,https://github.com/Stick-To/RefineDet-tensorflow,N
single-shot refinement neural network for object detection,https://github.com/ginn24/RefineDet,Y
single-shot refinement neural network for object detection,https://github.com/laycoding/FaceDetection,Y
single-shot refinement neural network for object detection,https://github.com/laycoding/renlianjiance,Y
gradient harmonized single-stage detector,https://github.com/xialuxi/GHMLoss-caffe,Y
gradient harmonized single-stage detector,https://github.com/libuyu/GHM_Detection,Y
deformable convolutional networks,https://github.com/zdaxie/DiDi,Y
deformable convolutional networks,https://github.com/qilei123/fpn_crop_v2_4d,Y
deformable convolutional networks,https://github.com/DHZS/tf-deformable-conv-layer,Y
deformable convolutional networks,https://github.com/qilei123/fpn_crop,Y
deformable convolutional networks,https://github.com/qilei123/DeformableConvV2,Y
deformable convolutional networks,https://github.com/makefile/frcnn,Y
deformable convolutional networks,https://github.com/MonsterPeng/Deformable-ConvNets-master,Y
deformable convolutional networks,https://github.com/NVIDIAAICITYCHALLENGE/AICity_Team6_ISU,Y
deformable convolutional networks,https://github.com/oeway/pytorch-deform-conv,Y
deformable convolutional networks,https://github.com/guanfuchen/Deformable-ConvNets,Y
deformable convolutional networks,https://github.com/fourmi1995/IronExperiment-DCN,Y
deformable convolutional networks,https://github.com/qilei123/DEEPLAB_4_RETINA,Y
deformable convolutional networks,https://github.com/zzdxfei/defor_conv_mxnet_code,Y
deformable convolutional networks,https://github.com/liyemei/Deformable-active-convolutional,Y
deformable convolutional networks,https://github.com/stupidZZ/pyc_repo,Y
deformable convolutional networks,https://github.com/qilei123/DeformableConvV2_crop,Y
deformable convolutional networks,https://github.com/qilei123/sod_v1_demo,Y
deformable convolutional networks,https://github.com/zengzhaoyang/trident,Y
deformable convolutional networks,https://github.com/qilei123/fpn_crop_v1_5d,Y
deformable convolutional networks,https://github.com/msracver/Deformable-ConvNets,Y
deformable convolutional networks,https://github.com/zengzhaoyang/Weak_Detection,Y
deformable convolutional networks,https://github.com/xiaoyongzhu/Deformable-ConvNets,Y
deformable convolutional networks,https://github.com/qilei123/DEEPLAB_4_RETINAIMG,Y
deformable convolutional networks,https://github.com/chenbys/GuidedOffset,Y
deformable convolutional networks,https://github.com/qilei123/sod_v1,Y
focal loss for dense object detection,https://github.com/williamccondori/darknet-alexey,N
focal loss for dense object detection,https://github.com/dc17540/darknet,Y
focal loss for dense object detection,https://github.com/soccergame/darknet,Y
focal loss for dense object detection,https://github.com/yytang2012/darknet,Y
focal loss for dense object detection,https://github.com/pierluigiferrari/ssd_keras,Y
focal loss for dense object detection,https://github.com/wpsliu123/yolo-windows,Y
focal loss for dense object detection,https://github.com/EMsnap/RobotSorting,Y
focal loss for dense object detection,https://github.com/tianhai123/yolov3,Y
focal loss for dense object detection,https://github.com/li-yibing/oceannet,Y
focal loss for dense object detection,https://github.com/khaled2ahmed/k2a,Y
focal loss for dense object detection,https://github.com/vuongtrannguyenkhoi/darknet,Y
focal loss for dense object detection,https://github.com/darshans0200/YOLOTest,N
focal loss for dense object detection,https://github.com/edward2306/dark,Y
focal loss for dense object detection,https://github.com/ruinmessi/RFBNet,Y
focal loss for dense object detection,https://github.com/zj463261929/darknet_mAP,Y
focal loss for dense object detection,https://github.com/kyunghwan/darknet_v3,Y
focal loss for dense object detection,https://github.com/kke0217/darknet_WIN,Y
focal loss for dense object detection,https://github.com/SteveBetter/darknet_ac,Y
focal loss for dense object detection,https://github.com/Sushma07/dancedarknet,Y
focal loss for dense object detection,https://github.com/williamccondori/YOLO-NFPA,Y
focal loss for dense object detection,https://github.com/slopezRedfox/CoffeeCoffee,Y
focal loss for dense object detection,https://github.com/vantupham/darknet,Y
focal loss for dense object detection,https://github.com/deepakHonakeri05/darknet,Y
focal loss for dense object detection,https://github.com/zhuqiang00099/abyolov3,Y
focal loss for dense object detection,https://github.com/buptdbj/darknet-windows-linux,Y
focal loss for dense object detection,https://github.com/metu-kovan/METU-ALET,Y
focal loss for dense object detection,https://github.com/ShaharrHorn/Detection-Game-logic,Y
focal loss for dense object detection,https://github.com/liyemei/Deformable-active-convolutional,Y
focal loss for dense object detection,https://github.com/marvis/pytorch-yolo2,Y
focal loss for dense object detection,https://github.com/unsky/focal-loss,Y
focal loss for dense object detection,https://github.com/ryubidragonfire/mydarknet,Y
focal loss for dense object detection,https://github.com/skylstat/AtlasProteinChallenge,Y
focal loss for dense object detection,https://github.com/bharatsush/object_detection,Y
focal loss for dense object detection,https://github.com/hemp110/darknet-myb,Y
focal loss for dense object detection,https://github.com/yhenon/pytorch-retinanet,Y
focal loss for dense object detection,https://github.com/maorshutman/human-protein-atlas-kaggle,Y
focal loss for dense object detection,https://github.com/tensorflow/models/tree/master/research/object_detection,Y
focal loss for dense object detection,https://github.com/anookeen/yolo,Y
focal loss for dense object detection,https://github.com/KingBoyBIT/yolov3test,Y
focal loss for dense object detection,https://github.com/saber2011/darknet,Y
focal loss for dense object detection,https://github.com/Cadmium-CD/socFinal,Y
focal loss for dense object detection,https://github.com/saednasir/Helmet_Yolo,Y
focal loss for dense object detection,https://github.com/teemoeric/projet,Y
focal loss for dense object detection,https://github.com/unsky/RetinaNet,Y
focal loss for dense object detection,https://github.com/toufiksk/darknet,Y
focal loss for dense object detection,https://github.com/yudie433/darknet,Y
focal loss for dense object detection,https://github.com/hankpark0706/darknet,Y
focal loss for dense object detection,https://github.com/manishravula/yolov3_tiny_pruned,Y
focal loss for dense object detection,https://github.com/xiamenwcy/extended-caffe,Y
focal loss for dense object detection,https://github.com/fizyr/keras-retinanet,Y
focal loss for dense object detection,https://github.com/DonCorle0ne/damage_detection,Y
focal loss for dense object detection,https://github.com/ghaniskn/GCorp-Darknet,Y
focal loss for dense object detection,https://github.com/rnirdhar/yoloMultiClass,N
focal loss for dense object detection,https://github.com/facebookresearch/detectron,N
focal loss for dense object detection,https://github.com/eric-erki/darknet,Y
focal loss for dense object detection,https://github.com/mathieuorhan/darknet,Y
focal loss for dense object detection,https://github.com/hallditq/Darknet,N
focal loss for dense object detection,https://github.com/xuanyuzhou98/SqueezeSeg,N
focal loss for dense object detection,https://github.com/ermuur/darknet,Y
focal loss for dense object detection,https://github.com/xiaodongww/SenceClassification-AIChallenge,Y
focal loss for dense object detection,https://github.com/bobby20180331/darknet_pycharm,Y
focal loss for dense object detection,https://github.com/eric-erki/yolov3,Y
focal loss for dense object detection,https://github.com/kwon852456/darkent,N
focal loss for dense object detection,https://github.com/aminekha/AI-For-2022,Y
focal loss for dense object detection,https://github.com/PolarisAlpha/darknet,Y
focal loss for dense object detection,https://github.com/neshitov/UNet,Y
focal loss for dense object detection,https://github.com/rnirdhar/yoloTestOneClass,N
focal loss for dense object detection,https://github.com/trongnghia00/darknet,Y
focal loss for dense object detection,https://github.com/yijiaceline/Final-Project-Group4,Y
acquisition of localization confidence for accurate object detection,https://github.com/CSAILVision/unifiedparsing,Y
acquisition of localization confidence for accurate object detection,https://github.com/vacancy/PreciseRoIPooling,Y
bounding box regression with uncertainty for accurate object detection,https://github.com/yihui-he/KL-Loss,N
bounding box regression with uncertainty for accurate object detection,https://github.com/yihui-he/softer-NMS,Y
mask r-cnn,https://github.com/qq330488563/TEST,Y
mask r-cnn,https://github.com/kdethoor/panoptictorch,Y
mask r-cnn,https://github.com/jianing-sun/Mask-YOLO,N
mask r-cnn,https://github.com/Arthur-Shi/py-faster-rcnn,Y
mask r-cnn,https://github.com/collectionslab/annotations-computervision,Y
mask r-cnn,https://github.com/shuto-keio/detection_person,N
mask r-cnn,https://github.com/leochangzliao/OPBM,Y
mask r-cnn,https://github.com/xzabg/faster-rcnn-with-Caltech,Y
mask r-cnn,https://github.com/collectionslab/book-annotation-classification,Y
mask r-cnn,https://github.com/rickyHong/py-faster-rcnn-repl,Y
mask r-cnn,https://github.com/UPCLJ/py-faster-rcnn,Y
mask r-cnn,https://github.com/zhangchi9/Airbus_Ship_Detection,Y
mask r-cnn,https://github.com/noelcodes/Mask_RCNN,Y
mask r-cnn,https://github.com/leonardhan1979/fasterRCNN,Y
mask r-cnn,https://github.com/jasjeetIM/Mask-RCNN,Y
mask r-cnn,https://github.com/stanleycelestin1/AirsimDetectron,N
mask r-cnn,https://github.com/tensorpack/tensorpack,N
mask r-cnn,https://github.com/lincaiming/py-faster-rcnn-update,Y
mask r-cnn,https://github.com/evaristr/py-faster_rcnn,Y
mask r-cnn,https://github.com/beassssry/U,Y
mask r-cnn,https://github.com/BlackAngel1111/Fast-RCNN,Y
mask r-cnn,https://github.com/xunhen/py-faster-rcnn-wjc,Y
mask r-cnn,https://github.com/DeNA/Chainer_Mask_R-CNN,Y
mask r-cnn,https://github.com/waspinator/deep-learning-explorer,Y
mask r-cnn,https://github.com/bethgelab/siamese-mask-rcnn,Y
mask r-cnn,https://github.com/jooyounghun/AI-Team-5,Y
mask r-cnn,https://github.com/zly9844/py-faster-rcnn,Y
mask r-cnn,https://github.com/matterport/Mask_RCNN,Y
mask r-cnn,https://github.com/zhong110020/py-faster-rcnn,Y
mask r-cnn,https://github.com/AKASH2907/bird_species_classification,Y
mask r-cnn,https://github.com/sunqiangxtcsun/faster-rcnn,Y
mask r-cnn,https://github.com/ishanashastri/FRCNN-for-breast-tumor-detection,N
mask r-cnn,https://github.com/CarstenIsert/DeepBurn,Y
mask r-cnn,https://github.com/sbetageri/MaskRCNN,Y
mask r-cnn,https://github.com/delldu/MaskRCNN,Y
mask r-cnn,https://github.com/collectionslab/Omniscribe,Y
mask r-cnn,https://github.com/qilei123/pyfasterrcnn,Y
mask r-cnn,https://github.com/lichengunc/mask-faster-rcnn,Y
mask r-cnn,https://github.com/tensorflow/models/tree/master/research/object_detection,Y
mask r-cnn,https://github.com/rickyHong/py-faster-rcnn-repl-cudnn5-support,Y
mask r-cnn,https://github.com/yczhang1017/SSD_resnet_pytorch,Y
mask r-cnn,https://github.com/TuSimple/mx-maskrcnn,Y
mask r-cnn,https://github.com/fdac18/ForensicImages,Y
mask r-cnn,https://github.com/soulguy/Faster-rcnn,Y
mask r-cnn,https://github.com/lincaiming/py-faster-rcnn-windows,Y
mask r-cnn,https://github.com/godspeedcurry/lung-nodule-detection,Y
mask r-cnn,https://github.com/charlesYangM/py-faster-rcnn-80.28,Y
mask r-cnn,https://github.com/AKASH2907/bird-species-classification,Y
mask r-cnn,https://github.com/facebookresearch/detectron,N
mask r-cnn,https://github.com/busyboxs/What-I-have-star,Y
mask r-cnn,https://github.com/guanfuchen/py-faster-rcnn,Y
mask r-cnn,https://github.com/phtruongan/py-faster-rcnn-docker,Y
mask r-cnn,https://github.com/busyboxs/faster_rcnn_voc,Y
mask r-cnn,https://github.com/pvdhove/owl-mask-rcnn,Y
mask r-cnn,https://github.com/cokowpublic/Nuclei-Segmentation,Y
chainercv: a library for deep learning in computer vision,https://github.com/chainer/chainercv,Y
chainercv: a library for deep learning in computer vision,https://github.com/pfnet/chainercv,Y
beyond skip connections: top-down modulation for object detection,https://github.com/MTCloudVision/mxnet-dssd,Y
feature pyramid networks for object detection,https://github.com/xinpingwang/tf-faster-rcnn,Y
feature pyramid networks for object detection,https://github.com/yzgrfsy/tf-fastrcnn-crop,Y
feature pyramid networks for object detection,https://github.com/1512159/tf-faster-rcnn-medico,Y
feature pyramid networks for object detection,https://github.com/sonnguyen64/horus-tf-faster-rcnn,Y
feature pyramid networks for object detection,https://github.com/huan123/py-fatser-rcnn,Y
feature pyramid networks for object detection,https://github.com/rickyHong/faster-rcnn-tensorflow-repl,Y
feature pyramid networks for object detection,https://github.com/Sunnyhoster/tf-faster-rcnn,Y
feature pyramid networks for object detection,https://github.com/tusimple/simpledet,N
feature pyramid networks for object detection,https://github.com/ruotianluo/pytorch-faster-rcnn,Y
feature pyramid networks for object detection,https://github.com/tensorpack/tensorpack,N
feature pyramid networks for object detection,https://github.com/1512159/demo_frcnn,Y
feature pyramid networks for object detection,https://github.com/yfnn/fused-model,Y
feature pyramid networks for object detection,https://github.com/nautilus261/tf-faster-rcnn,N
feature pyramid networks for object detection,https://github.com/zhoujqhappy/simpledet_int8,N
feature pyramid networks for object detection,https://github.com/mengyingfei/faster-rcnn-tf,Y
feature pyramid networks for object detection,https://github.com/cantaible/tf-faster-rcnn,Y
feature pyramid networks for object detection,https://github.com/guilherme-pombo/keras_resnext_fpn,Y
feature pyramid networks for object detection,https://github.com/PengchengAi/tf-faster-rcnn-pcai,Y
feature pyramid networks for object detection,https://github.com/csmiler/tf-faster-rcnn-cpu,Y
feature pyramid networks for object detection,https://github.com/insigh/Faster-RCNN-Tensorflow,Y
feature pyramid networks for object detection,https://github.com/spideralessio/coconutnut,Y
feature pyramid networks for object detection,https://github.com/unsky/FPN,Y
feature pyramid networks for object detection,https://github.com/yanxp/rcnn-discovery,Y
feature pyramid networks for object detection,https://github.com/ithuanhuan/gpu-py3-faster-rcnn,Y
feature pyramid networks for object detection,https://github.com/soeaver/py-RFCN-priv,Y
feature pyramid networks for object detection,https://github.com/rickyHong/FPN-repl,Y
feature pyramid networks for object detection,https://github.com/endernewton/tf-faster-rcnn,Y
feature pyramid networks for object detection,https://github.com/Angzz/fpn-gluon-cv,Y
feature pyramid networks for object detection,https://github.com/PythonZhengbinChen/tf-faster-rcnn-cpu,Y
feature pyramid networks for object detection,https://github.com/Kenneth-Wong/tf-faster-rcnn,Y
feature pyramid networks for object detection,https://github.com/rickyHong/pytorch-faster-rcnn-repl,Y
feature pyramid networks for object detection,https://github.com/bareblackfoot/lddp-tf-faster-rcnn,N
feature pyramid networks for object detection,https://github.com/anlimo1510/faster-rcnn,Y
feature pyramid networks for object detection,https://github.com/facebookresearch/detectron,N
feature pyramid networks for object detection,https://github.com/YudeWang/UNet-Satellite-Image-Segmentation,Y
feature pyramid networks for object detection,https://github.com/libiseller/MobileNetV2-dynamicFPN,Y
feature pyramid networks for object detection,https://github.com/achiyaj/vqa-sandbox,Y
feature pyramid networks for object detection,https://github.com/yfnn/person_subclass,Y
feature pyramid networks for object detection,https://github.com/neuqgz/modify-faster-rcnn-tf,Y
feature pyramid networks for object detection,https://github.com/chcyj/my-project,Y
feature pyramid networks for object detection,https://github.com/daxiapazi/faster-rcnn,Y
feature pyramid networks for object detection,https://github.com/cantaible/tf-faster-rcnn-note,Y
feature pyramid networks for object detection,https://github.com/ChestnutLi/tf-faster-rcnn,Y
feature pyramid networks for object detection,https://github.com/ithuanhuan/py-fatser-rcnn,Y
feature pyramid networks for object detection,https://github.com/zxqcreations/faster-rcnn-tf,Y
deep residual learning for image recognition,https://github.com/Kunaldargan/Paper-summerization--DEEP-LEARNING-MADE-EASIER-BY-LINEAR-TRANSFORMATIONS-IN-PERCEPTRONS--,Y
deep residual learning for image recognition,https://github.com/Sangkwun/Resnet,Y
deep residual learning for image recognition,https://github.com/tensorflow/models/tree/master/research/deeplab,Y
deep residual learning for image recognition,https://github.com/akshaytess/deep,Y
deep residual learning for image recognition,https://github.com/xcmyz/Face-Binary-Classification,Y
deep residual learning for image recognition,https://github.com/itijyou/ademxapp,Y
deep residual learning for image recognition,https://github.com/glouppe/info8010-deep-learning,Y
deep residual learning for image recognition,https://github.com/kwotsin/TensorFlow-ENet,Y
deep residual learning for image recognition,https://github.com/akamaster/pytorch_resnet_cifar10,Y
deep residual learning for image recognition,https://github.com/swasun/VQ-VAE-images,Y
deep residual learning for image recognition,https://github.com/andreasveit/densenet-pytorch,Y
deep residual learning for image recognition,https://github.com/ferhatkkochan/pneumonia_classifier,N
deep residual learning for image recognition,https://github.com/eremo2002/Keras-ResNet,Y
deep residual learning for image recognition,https://github.com/wsqiankun/resnet_tensorflow,Y
deep residual learning for image recognition,https://github.com/eric-erki/IdenProf,Y
deep residual learning for image recognition,https://github.com/hycis/TensorGraph,Y
deep residual learning for image recognition,https://github.com/sebsquire/Dogs-and-cats-image-classification-CNN,Y
deep residual learning for image recognition,https://github.com/deepmodeling/deepmd-kit,Y
deep residual learning for image recognition,https://github.com/fchollet/deep-learning-models,Y
deep residual learning for image recognition,https://github.com/rohit517/Deep-Learning,N
deep residual learning for image recognition,https://github.com/jimheaton/Ultra96_ML_Embedded_Workshop,Y
deep residual learning for image recognition,https://github.com/tensorflow/models/tree/master/research/resnet,N
deep residual learning for image recognition,https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implmentation,Y
deep residual learning for image recognition,https://github.com/CodeChefVIT/VOID,Y
deep residual learning for image recognition,https://github.com/Punakshi/Deep-Residual-Learning-for-Image-Recognition-Implementation,Y
deep residual learning for image recognition,https://github.com/hysts/pytorch_resnet_preact,Y
deep residual learning for image recognition,https://github.com/hx19940102/Image-Segmentation-based-on-FCN,N
deep residual learning for image recognition,https://github.com/rickyHong/tfRecord-Caltech256-repl2,Y
deep residual learning for image recognition,https://github.com/brycexu/MR-Residual-Net,Y
deep residual learning for image recognition,https://github.com/akar5h/Image-Recognition-with-CNN-and-Transfer-Learning-ResNet101,Y
deep residual learning for image recognition,https://github.com/b8goal/PIRL-Residual-network,Y
deep residual learning for image recognition,https://github.com/lixihan/Resnet,Y
deep residual learning for image recognition,https://github.com/tensorpack/tensorpack,N
deep residual learning for image recognition,https://github.com/saloni1998/Cifar-10,Y
deep residual learning for image recognition,https://github.com/iArunava/ResNet,Y
deep residual learning for image recognition,https://github.com/DaikiTanak/manifold_mixup,Y
deep residual learning for image recognition,https://github.com/jtcass01/SIMON,Y
deep residual learning for image recognition,https://github.com/sanoyo/dl-model,Y
deep residual learning for image recognition,https://github.com/nitinvwaran/UrbanSound8K-audio-classification-with-ResNet,Y
deep residual learning for image recognition,https://github.com/mr-bulb/DL_basic_github,Y
deep residual learning for image recognition,https://github.com/danielparada1/RL_SpaceInvaders,Y
deep residual learning for image recognition,https://github.com/suhas1999/Flip-kart-grid-challenge,Y
deep residual learning for image recognition,https://github.com/osmr/imgclsmob,Y
deep residual learning for image recognition,https://github.com/ankit-vaghela30/Google-landmark-prediction,Y
deep residual learning for image recognition,https://github.com/xcmyz/FaceDetection,Y
deep residual learning for image recognition,https://github.com/Baichenjia/Resnet,Y
deep residual learning for image recognition,https://github.com/D-X-Y/ResNeXt-DenseNet,Y
deep residual learning for image recognition,https://github.com/fastai/fastai,Y
deep residual learning for image recognition,https://github.com/tensorflow/models/tree/master/research/slim,Y
deep residual learning for image recognition,https://github.com/UnofficialJuliaMirror/DeepMark-deepmark,Y
deep residual learning for image recognition,https://github.com/syiin/ann_cancer_detection,Y
deep residual learning for image recognition,https://github.com/shoaibahmed/CrossLayerPooling,Y
deep residual learning for image recognition,https://github.com/anibali/dsnt-pose2d,Y
deep residual learning for image recognition,https://github.com/sebsquire/FlowersRecognition_keras,Y
deep residual learning for image recognition,https://github.com/EmolLi/Random,N
deep residual learning for image recognition,https://github.com/makatx/YOLO_ResNet,Y
deep residual learning for image recognition,https://github.com/gcr/torch-residual-networks,Y
deep residual learning for image recognition,https://github.com/astoycos/Mini_Project2,Y
deep residual learning for image recognition,https://github.com/phillity/BioCapsule,N
deep residual learning for image recognition,https://github.com/shtnkgm/VisionFrameworkSample,Y
deep residual learning for image recognition,https://github.com/nsom/Basic-Models,Y
deep residual learning for image recognition,https://github.com/vlievin/Unet,Y
deep residual learning for image recognition,https://github.com/alrojo/lasagne_residual_network,Y
deep residual learning for image recognition,https://github.com/OlafenwaMoses/IdenProf,Y
deep residual learning for image recognition,https://github.com/koshian2/ResNet-MultipleFramework,Y
deep residual learning for image recognition,https://github.com/hongyi-zhang/Fixup,Y
deep residual learning for image recognition,https://github.com/MichaelWangfc/distributed-tensorflow-resnet,N
deep residual learning for image recognition,https://github.com/t0930198/backup,Y
deep residual learning for image recognition,https://github.com/facebook/fb.resnet.torch,Y
deep residual learning for image recognition,https://github.com/researchmm/SiamDW,Y
deep residual learning for image recognition,https://github.com/CrossEntropy/Gesture-images-recognition-ResidualNetwork,Y
deep residual learning for image recognition,https://github.com/JesonZhang822/Dogs-VS-Cats-Project,Y
deep residual learning for image recognition,https://github.com/mbadry1/Top-Deep-Learning,N
deep residual learning for image recognition,https://github.com/uclaml/Padam,Y
deep residual learning for image recognition,https://github.com/nsom/VGG16,Y
deep residual learning for image recognition,https://github.com/Glp91/fire-detection,Y
deep residual learning for image recognition,https://github.com/leemathew1998/RG,Y
deep residual learning for image recognition,https://github.com/c1ph3rr/Deep-Residual-Learning-for-Image-Recognition,N
deep residual learning for image recognition,https://github.com/JHLee0513/Salt_detection_challenge,Y
deep residual learning for image recognition,https://github.com/zlenyk/NNCodesAndProjects,Y
deep residual learning for image recognition,https://github.com/xcmyz/Face_Binary_Classfication,Y
deep residual learning for image recognition,https://github.com/CVxTz/face_age_gender,Y
deep residual learning for image recognition,https://github.com/syiin/resnet_cancer_detection,Y
deep residual learning for image recognition,https://github.com/varshaneya/Res-SE-Net,Y
deep residual learning for image recognition,https://github.com/wenxinxu/resnet-in-tensorflow,Y
deep residual learning for image recognition,https://github.com/FranckZibi/SharpNet,Y
deep residual learning for image recognition,https://github.com/ajithvallabai/getsetgo_keras-beginner,Y
deep residual learning for image recognition,https://github.com/ChangweiZhang/Awesome-WindowsML-ONNX-Models,N
deep residual learning for image recognition,https://github.com/danielamassiceti/CCA-visualdialogue,Y
deep residual learning for image recognition,https://github.com/GKalliatakis/Keras-VGG16-places365,Y
deep residual learning for image recognition,https://github.com/UnofficialJuliaMirrorSnapshots/DeepMark-deepmark,Y
deep residual learning for image recognition,https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implementation,Y
deep residual learning for image recognition,https://github.com/MaxwellHeller/coffee_fraud_detection,Y
deep residual learning for image recognition,https://github.com/DeepMark/deepmark,Y
deep residual learning for image recognition,https://github.com/jonnedtc/Shake-Shake-Keras,N
deep residual learning for image recognition,https://github.com/horse007666/ResNet,Y
deep residual learning for image recognition,https://github.com/facebookresearch/detectron,N
deep residual learning for image recognition,https://github.com/YudeWang/UNet-Satellite-Image-Segmentation,Y
deep residual learning for image recognition,https://github.com/thughost2/Padam,Y
deep residual learning for image recognition,https://github.com/hershd23/ObjectLocalizer,Y
deep residual learning for image recognition,https://github.com/wuqiyao20160118/mini_project-for-CIFAR10,Y
deep residual learning for image recognition,https://github.com/julik43/IIMAS-USCS,Y
deep residual learning for image recognition,https://github.com/asprenger/keras_fc_densenet,Y
speed/accuracy trade-offs for modern convolutional object detectors,https://github.com/tensorflow/models/tree/master/research/object_detection,Y
speed/accuracy trade-offs for modern convolutional object detectors,https://github.com/felixlephuoc/Annotating-Images-Object-Detection-API,Y
speed/accuracy trade-offs for modern convolutional object detectors,https://github.com/suryapa1/MAX-Object-Detector-v1,Y
speed/accuracy trade-offs for modern convolutional object detectors,https://github.com/IBM/MAX-Object-Detector,Y
a multipath network for object detection,https://github.com/facebookresearch/multipathnet,Y
going deeper with convolutions,https://github.com/vijayvee/behavior-recognition,Y
going deeper with convolutions,https://github.com/GenesisDCarmen/C_Reconocimiento_Facial,Y
going deeper with convolutions,https://github.com/Stick-To/Inception,N
going deeper with convolutions,https://github.com/kaishengtai/neuralart,Y
going deeper with convolutions,https://github.com/vijayvee/behavior_recognition,Y
going deeper with convolutions,https://github.com/oci-ai/oci-dreamer,Y
going deeper with convolutions,https://github.com/facebook/fb-caffe-exts,Y
going deeper with convolutions,https://github.com/lim0606/caffe-googlenet-bn,Y
going deeper with convolutions,https://github.com/leoliao2008/TensorflowMobileOfficialExample,Y
going deeper with convolutions,https://github.com/ajithvallabai/getsetgo_keras-beginner,Y
going deeper with convolutions,https://github.com/JasonLiTW/simple-railway-captcha-solver,Y
going deeper with convolutions,https://github.com/kevin28520/GoogleNet,Y
going deeper with convolutions,https://github.com/JavierAntoran/pictocatcher,Y
going deeper with convolutions,https://github.com/MohamedRamzy1/Inception,Y
going deeper with convolutions,https://github.com/jimheaton/Ultra96_ML_Embedded_Workshop,Y
going deeper with convolutions,https://github.com/Adren98/EczemaApp,Y
going deeper with convolutions,https://github.com/deepmind/kinetics-i3d,Y
going deeper with convolutions,https://github.com/tiger154/captcha-solver-custom,Y
going deeper with convolutions,https://github.com/tensorflow/models/tree/master/research/slim,Y
going deeper with convolutions,https://github.com/Liuyubao/transfer-learning,Y
going deeper with convolutions,https://github.com/google/prettytensor,Y
going deeper with convolutions,https://github.com/facebook/fb.resnet.torch,Y
going deeper with convolutions,https://github.com/researchmm/SiamDW,Y
"overfeat: integrated recognition, localization and detection using convolutional networks",https://github.com/soumith/convnet-benchmarks,N
"overfeat: integrated recognition, localization and detection using convolutional networks",https://github.com/soumith/imagenet-multiGPU.torch,N
"overfeat: integrated recognition, localization and detection using convolutional networks",https://github.com/sermanet/OverFeat,N
"overfeat: integrated recognition, localization and detection using convolutional networks",https://github.com/vohoaiviet/OverFeat-DeepNet,N
couplenet: coupling global structure with local parts for object detection,https://github.com/tshizys/CoupleNet,N
couplenet: coupling global structure with local parts for object detection,https://github.com/princewang1994/RFCN_CoupleNet.pytorch,N
spatial pyramid pooling in deep convolutional networks for visual recognition,https://github.com/yhenon/keras-spp,N
spatial pyramid pooling in deep convolutional networks for visual recognition,https://github.com/szagoruyko/imagine-nn,N
spatial pyramid pooling in deep convolutional networks for visual recognition,https://github.com/khayne31/CNN-with-Spacial-Pyramid-Pooling-Implementation,N
spatial pyramid pooling in deep convolutional networks for visual recognition,https://github.com/rajkumargithub/densenet.mura,N
spatial pyramid pooling in deep convolutional networks for visual recognition,https://github.com/xiamenwcy/extended-caffe,N
training region-based object detectors with online hard example mining,https://github.com/busyboxs/Some-resources-useful-for-me,Y
training region-based object detectors with online hard example mining,https://github.com/tkuanlun350/Kaggle_Ship_Detection_2018,Y
denet: scalable real-time object detection with directed sparse sampling,https://github.com/lachlants/denet,N
a-fast-rcnn: hard positive generation via adversary for object detection,https://github.com/busyboxs/Some-resources-useful-for-me,Y
a-fast-rcnn: hard positive generation via adversary for object detection,https://github.com/xzabg/fast-adversarial,Y
a-fast-rcnn: hard positive generation via adversary for object detection,https://github.com/HusterRC/adversarial-frcnn-master,Y
a-fast-rcnn: hard positive generation via adversary for object detection,https://github.com/xiaolonw/adversarial-frcnn,Y
fast r-cnn,https://github.com/sbetageri/MaskRCNN,Y
fast r-cnn,https://github.com/saumya-jetley/pp_ICVSS15_TacklingBkgndDifferently,Y
fast r-cnn,https://github.com/qq330488563/TEST,Y
fast r-cnn,https://github.com/facebookresearch/detectron,N
fast r-cnn,https://github.com/rbgirshick/fast-rcnn,Y
fast r-cnn,https://github.com/beassssry/U,Y
fast r-cnn,https://github.com/BlackAngel1111/Fast-RCNN,Y
fast r-cnn,https://github.com/nirajdevpandey/Object-detection-and-localization-using-SSD-,Y
fast r-cnn,https://github.com/AkashGanesan/PedestrianAttention,Y
subcategory-aware convolutional neural networks for object proposals and detection,https://github.com/xiaohaoChen/rrc_detection,N
multi-view silhouette and depth decomposition for high resolution 3d object representation,https://github.com/kingcheng2000/Multi-View-Silhouette-and-Depth-Decomposition-for-High-Resolution-3D-Object-Representation,N
multi-view silhouette and depth decomposition for high resolution 3d object representation,https://github.com/EdwardSmith1884/3D-Object-Super-Resolution,N
multi-view silhouette and depth decomposition for high resolution 3d object representation,https://github.com/EdwardSmith1884/Multi-View-Silhouette-and-Depth-Decomposition-for-High-Resolution-3D-Object-Representation,N
pixel2mesh: generating 3d mesh models from single rgb images,https://github.com/nywang16/Pixel2Mesh,N
a point set generation network for 3d object reconstruction from a single image,https://github.com/pointcloudlearning/3D-Deep-Learning-Paper-List,N
3d-r2n2: a unified approach for single and multi-view 3d object reconstruction,https://github.com/Amaranth819/3dr2n2-tensorflow,N
neural 3d mesh renderer,https://github.com/laughtervv/tf_neural_renderer,N
a mention-ranking model for abstract anaphora resolution,https://github.com/amarasovic/neural-abstract-anaphora,N
an integral pose regression system for the eccv2018 posetrack challenge,https://github.com/JimmySuen/integral-human-pose,Y
self-supervised learning of 3d human pose using multi-view geometry,https://github.com/mkocabas/EpipolarPose,N
neural body fitting: unifying deep learning and model-based human pose and shape estimation,https://github.com/mohomran/neural_body_fitting,N
towards 3d human pose estimation in the wild: a weakly-supervised approach,https://github.com/lxy5513/pose-hg-3d,N
towards 3d human pose estimation in the wild: a weakly-supervised approach,https://github.com/xingyizhou/pose-hg-3d,N
towards 3d human pose estimation in the wild: a weakly-supervised approach,https://github.com/xingyizhou/pytorch-pose-hg-3d,N
exploiting temporal information for 3d pose estimation,https://github.com/rayat137/Pose_3D,N
lifting from the deep: convolutional 3d pose estimation from a single image,https://github.com/DenisTome/Lifting-from-the-Deep-release,N
lifting from the deep: convolutional 3d pose estimation from a single image,https://github.com/SyBorg91/pose-estimation-detection,N
deep high-resolution representation learning for human pose estimation,https://github.com/leoxiaobin/deep-high-resolution-net.pytorch,Y
simple baselines for human pose estimation and tracking,https://github.com/simochen/flowtrack.pytorch,Y
simple baselines for human pose estimation and tracking,https://github.com/Microsoft/human-pose-estimation.pytorch,Y
simple baselines for human pose estimation and tracking,https://github.com/leoxiaobin/pose.pytorch,N
simple baselines for human pose estimation and tracking,https://github.com/victimsnino/Simple-Baselines-for-Human-Pose-Estimation-and-Tracking-sample-,Y
simple baselines for human pose estimation and tracking,https://github.com/mks0601/TF-SimpleHumanPose,Y
multiposenet: fast multi-person pose estimation using pose residual network,https://github.com/eric-erki/pose-residual-network-pytorch,N
multiposenet: fast multi-person pose estimation using pose residual network,https://github.com/danielperezr88/multiposenet-aries,N
multiposenet: fast multi-person pose estimation using pose residual network,https://github.com/IcewineChen/pytorch-MultiPoseNet,N
multiposenet: fast multi-person pose estimation using pose residual network,https://github.com/salihkaragoz/pose-residual-network-pytorch,N
realtime multi-person 2d pose estimation using part affinity fields,https://github.com/zyxcambridge/openpose_all,Y
realtime multi-person 2d pose estimation using part affinity fields,https://github.com/CMU-Perceptual-Computing-Lab/openpose,Y
realtime multi-person 2d pose estimation using part affinity fields,https://github.com/Joyce511/OpenVino_Realtime_Multi-Person_Pose_Estimation,N
realtime multi-person 2d pose estimation using part affinity fields,https://github.com/DhruvJawalkar/yoga-pose-estimation,N
realtime multi-person 2d pose estimation using part affinity fields,https://github.com/MayankGupta73/Workout-Evaluation-tf-openpose,N
realtime multi-person 2d pose estimation using part affinity fields,https://github.com/subrapak/Pose-Estimation-For-Workouts,N
realtime multi-person 2d pose estimation using part affinity fields,https://github.com/yikegami/openpose,Y
realtime multi-person 2d pose estimation using part affinity fields,https://github.com/xinshuoweng/my2d_cpm,N
realtime multi-person 2d pose estimation using part affinity fields,https://github.com/ZheC/Realtime_Multi-Person_Pose_Estimation,N
realtime multi-person 2d pose estimation using part affinity fields,https://github.com/shihenw/convolutional-pose-machines-release,Y
realtime multi-person 2d pose estimation using part affinity fields,https://github.com/yinzhiyan43/openpose-dev,Y
realtime multi-person 2d pose estimation using part affinity fields,https://github.com/CMU-Perceptual-Computing-Lab/openpose_unity_plugin,Y
realtime multi-person 2d pose estimation using part affinity fields,https://github.com/sananand007/Xrelab,N
realtime multi-person 2d pose estimation using part affinity fields,https://github.com/radioactiverockets/OpenPoseBreackoutGame,Y
realtime multi-person 2d pose estimation using part affinity fields,https://github.com/lncarter/Openpose,Y
realtime multi-person 2d pose estimation using part affinity fields,https://github.com/AkashGanesan/PedestrianAttention,Y
realtime multi-person 2d pose estimation using part affinity fields,https://github.com/xaoch/rapJetson2,Y
realtime multi-person 2d pose estimation using part affinity fields,https://github.com/MSeeker1340/Vision2018-Pose,N
realtime multi-person 2d pose estimation using part affinity fields,https://github.com/krutikabapat/Deep-Learning-based-OpenPose-Estimation,N
realtime multi-person 2d pose estimation using part affinity fields,https://github.com/krishnac7/OpenPose,N
pifpaf: composite fields for human pose estimation,https://github.com/vita-epfl/openpifpaf,N
pifpaf: composite fields for human pose estimation,https://github.com/vita-epfl/openpifpafwebdemo,N
"personlab: person pose estimation and instance segmentation with a bottom-up, part-based, geometric embedding model",https://github.com/preritj/pose_estimation,N
associative embedding: end-to-end learning for joint detection and grouping,https://github.com/stevehjc/pose-ae-demo-tf,N
starmap for category-agnostic keypoint and viewpoint estimation,https://github.com/xingyizhou/StarMap,N
"deepercut: a deeper, stronger, and faster multi-person pose estimation model",https://github.com/PJunhyuk/people-counting-pose,N
"deepercut: a deeper, stronger, and faster multi-person pose estimation model",https://github.com/eldar/pose-tensorflow,N
"deepercut: a deeper, stronger, and faster multi-person pose estimation model",https://github.com/ayeshathanawalla/DeepLabCut,N
"deepercut: a deeper, stronger, and faster multi-person pose estimation model",https://github.com/lambdaloop/anipose,N
"deepercut: a deeper, stronger, and faster multi-person pose estimation model",https://github.com/toomanymat/pose_estimation,N
"deepercut: a deeper, stronger, and faster multi-person pose estimation model",https://github.com/rachit2403/Easy-Cut,N
"deepercut: a deeper, stronger, and faster multi-person pose estimation model",https://github.com/eldar/deepcut-cnn,Y
"deepercut: a deeper, stronger, and faster multi-person pose estimation model",https://github.com/PJunhyuk/exercise-pose-analyzer,N
"deepercut: a deeper, stronger, and faster multi-person pose estimation model",https://github.com/janbertelngo/count-people,N
"deepercut: a deeper, stronger, and faster multi-person pose estimation model",https://github.com/eldar/deepcut,Y
"deepercut: a deeper, stronger, and faster multi-person pose estimation model",https://github.com/orkqueen/depplabseongil,N
"deepercut: a deeper, stronger, and faster multi-person pose estimation model",https://github.com/mcamila777/DeepLabCut_custom,N
deepcut: joint subset partition and labeling for multi person pose estimation,https://github.com/eldar/deepcut,Y
deepcut: joint subset partition and labeling for multi person pose estimation,https://github.com/eldar/deepcut-cnn,Y
generative partition networks for multi-person pose estimation,https://github.com/NieXC/pytorch-ppn,N
rmpe: regional multi-person pose estimation,https://github.com/MVIG-SJTU/AlphaPose,N
rmpe: regional multi-person pose estimation,https://github.com/Fangyh09/pose_nms,N
rmpe: regional multi-person pose estimation,https://github.com/MVIG-SJTU/RMPE,N
arttrack: articulated multi-person tracking in the wild,https://github.com/PJunhyuk/people-counting-pose,Y
arttrack: articulated multi-person tracking in the wild,https://github.com/eldar/pose-tensorflow,Y
arttrack: articulated multi-person tracking in the wild,https://github.com/ayeshathanawalla/DeepLabCut,Y
arttrack: articulated multi-person tracking in the wild,https://github.com/toomanymat/pose_estimation,Y
arttrack: articulated multi-person tracking in the wild,https://github.com/rachit2403/Easy-Cut,Y
arttrack: articulated multi-person tracking in the wild,https://github.com/PJunhyuk/exercise-pose-analyzer,Y
arttrack: articulated multi-person tracking in the wild,https://github.com/janbertelngo/count-people,Y
arttrack: articulated multi-person tracking in the wild,https://github.com/mcamila777/DeepLabCut_custom,Y
multi-person pose estimation with local joint-to-person associations,https://github.com/MVIG-SJTU/RMPE,N
densefusion: 6d object pose estimation by iterative dense fusion,https://github.com/j96w/DenseFusion,N
posecnn: a convolutional neural network for 6d object pose estimation in cluttered scenes,https://github.com/yuxng/PoseCNN,N
posecnn: a convolutional neural network for 6d object pose estimation in cluttered scenes,https://github.com/victorphd/aa,N
real-time seamless single shot 6d object pose prediction,https://github.com/Microsoft/singleshotpose,N
real-time seamless single shot 6d object pose prediction,https://github.com/LungTakumi/SSPAndroid,N
implicit 3d orientation learning for 6d object detection from rgb images,https://github.com/DLR-RM/AugmentedAutoencoder,N
deepim: deep iterative matching for 6d pose estimation,https://github.com/liyi14/mx-DeepIM,N
estimating 6d pose from localizing designated surface keypoints,https://github.com/sjtuytc/betapose,N
estimating 6d pose from localizing designated surface keypoints,https://github.com/why2011btv/6d_pose_estimation,N
fine-grained head pose estimation without keypoints,https://github.com/natanielruiz/deep-head-pose,Y
"how far are we from solving the 2d & 3d face alignment problem? (and a dataset of 230,000 3d facial landmarks)",https://github.com/lippman1125/pytorch_FAN,N
"how far are we from solving the 2d & 3d face alignment problem? (and a dataset of 230,000 3d facial landmarks)",https://github.com/GuohongLi/face-alignment-pytorch,N
stacked hourglass networks for human pose estimation,https://github.com/mpapadomanolaki/HourGlass_Network,Y
stacked hourglass networks for human pose estimation,https://github.com/anibali/dsnt-pose2d,Y
stacked hourglass networks for human pose estimation,https://github.com/neherh/HyperStackNet,Y
stacked hourglass networks for human pose estimation,https://github.com/geopavlakos/c2f-vol-demo,Y
stacked hourglass networks for human pose estimation,https://github.com/MandyMo/pytorch_HMR,Y
stacked hourglass networks for human pose estimation,https://github.com/Barak123748596/CVDL_course_project,Y
stacked hourglass networks for human pose estimation,https://github.com/yuanyuanli85/Stacked_Hourglass_Network_Keras,Y
stacked hourglass networks for human pose estimation,https://github.com/umich-vl/pose-hg-demo,Y
stacked hourglass networks for human pose estimation,https://github.com/chanyn/3Dpose_ssl,Y
stacked hourglass networks for human pose estimation,https://github.com/varunvprabhu/simple_pose_hourglass,Y
stacked hourglass networks for human pose estimation,https://github.com/umich-vl/pose-hg-train,Y
stacked hourglass networks for human pose estimation,https://github.com/motlabs/dont-be-turtle,Y
convolutional pose machines,https://github.com/zyxcambridge/openpose_all,Y
convolutional pose machines,https://github.com/CMU-Perceptual-Computing-Lab/openpose,Y
convolutional pose machines,https://github.com/mayorquinmachines/YogAI,Y
convolutional pose machines,https://github.com/digital-thinking/deep-posemachine,N
convolutional pose machines,https://github.com/yikegami/openpose,Y
convolutional pose machines,https://github.com/shihenw/convolutional-pose-machines-release,Y
convolutional pose machines,https://github.com/yinzhiyan43/openpose-dev,Y
convolutional pose machines,https://github.com/CMU-Perceptual-Computing-Lab/openpose_unity_plugin,Y
convolutional pose machines,https://github.com/limi44/Parkinson-s-Pose-Estimation-Dataset,Y
convolutional pose machines,https://github.com/chanyn/3Dpose_ssl,Y
convolutional pose machines,https://github.com/radioactiverockets/OpenPoseBreackoutGame,Y
convolutional pose machines,https://github.com/lncarter/Openpose,Y
convolutional pose machines,https://github.com/xaoch/rapJetson2,Y
convolutional pose machines,https://github.com/smellslikeml/YogAI,Y
parsing r-cnn for instance-level human analysis,https://github.com/soeaver/Parsing-R-CNN,N
densepose: dense human pose estimation in the wild,https://github.com/chen-judge/DensePose_git,Y
densepose: dense human pose estimation in the wild,https://github.com/StupidmanTan/facebookresearch,Y
densepose: dense human pose estimation in the wild,https://github.com/facebookresearch/DensePose,Y
densepose: dense human pose estimation in the wild,https://github.com/facebookresearch/detectron,N
densepose: dense human pose estimation in the wild,https://github.com/freedombenLiu/DensePose,Y
densepose: dense human pose estimation in the wild,https://github.com/ARMUGHAN-SHAHID/MoboDensepose,Y
densepose: dense human pose estimation in the wild,https://github.com/M-Usman10/DenseSqueeze-RCNN,Y
densepose: dense human pose estimation in the wild,https://github.com/AkashGanesan/PedestrianAttention,Y
densepose: dense human pose estimation in the wild,https://github.com/svikramank/DensePose,Y
densepose: dense human pose estimation in the wild,https://github.com/lncarter/Dencepose,Y
towards viewpoint invariant 3d human pose estimation,https://github.com/mks0601/V2V-PoseNet_RELEASE,Y
towards viewpoint invariant 3d human pose estimation,https://github.com/pengfeiZhao1993/V2V-PoseNet,Y
learning feature pyramids for human pose estimation,https://github.com/wanggrun/Learning-Feature-Pyramids-For-COCO,Y
learning feature pyramids for human pose estimation,https://github.com/wanggrun/Learning-Feature-Pyramids,Y
learning feature pyramids for human pose estimation,https://github.com/IcewineChen/pytorch-PyraNet,Y
learning feature pyramids for human pose estimation,https://github.com/bearpaw/PyraNet,Y
learning feature pyramids for human pose estimation,https://github.com/sunguanxiong/PyraAttention,Y
knowledge-guided deep fractal neural networks for human pose estimation,https://github.com/Guanghan/GNet-pose,N
multi-context attention for human pose estimation,https://github.com/bearpaw/pose-attention,Y
human pose regression by combining indirect part detection and contextual information,https://github.com/dluvizon/pose-regression,N
quantized densely connected u-nets for efficient landmark localization,https://github.com/zhiqiangdon/CU-Net,Y
integral human pose regression,https://github.com/strawberryfg/c2f-3dhm-human-caffe,Y
integral human pose regression,https://github.com/JimmySuen/integral-human-pose,Y
cu-net: coupled u-nets,https://github.com/zhiqiangdon/CU-Net,Y
shorten spatial-spectral rnn with parallel-gru for hyperspectral image classification,https://github.com/codeRimoe/DL_for_RSIs,N
bass net: band-adaptive spectral-spatial feature learning neural network for hyperspectral image classification,https://github.com/kaustubh0mani/BASS-Net,N
hyperspectral image classification with markov random fields and a convolutional neural network,https://github.com/xiangyongcao/CNN_HSIC_MRF,N
a joint many-task model: growing a neural network for multiple nlp tasks,https://github.com/hassyGo/charNgram2vec,Y
a joint many-task model: growing a neural network for multiple nlp tasks,https://github.com/rubythonode/joint-many-task-model,Y
an amr aligner tuned by transition-based parser,https://github.com/Oneplus/tamr,Y
constituency parsing with a self-attentive encoder,https://github.com/nikitakit/self-attentive-parser,Y
an empirical study of building a strong baseline for constituency parsing,https://github.com/nttcslab-nlp/strong_s2s_baseline_parser,Y
parsing as language modeling,https://github.com/cdg720/emnlp2016,N
grammar as a foreign language,https://github.com/sunnysinghnitb/text_corrector_software,N
grammar as a foreign language,https://github.com/sunnysinghnitb/text-corrector-software,N
grammar as a foreign language,https://github.com/atpaino/deep-text-corrector,N
grammar as a foreign language,https://github.com/kmadathil/sanskrit_parser,Y
grammar as a foreign language,https://github.com/gongshuangshuang/deep-text-corrector,N
grammar as a foreign language,https://github.com/sambit9238/deep_text_corrector,N
grammar as a foreign language,https://github.com/PiyKat/Grammar-Checker,N
recurrent neural network grammars,https://github.com/gofortargets/rnng,Y
recurrent neural network grammars,https://github.com/clab/rnng,Y
regularizing face verification nets for pain intensity regression,https://github.com/happynear/PainRegression,Y
capsule graph neural network,https://github.com/benedekrozemberczki/CapsGNN,N
learning convolutional neural networks for graphs,https://github.com/TibiG97/Part2Project,N
learning convolutional neural networks for graphs,https://github.com/tvayer/PSCN,Y
learning convolutional neural networks for graphs,https://github.com/CielAl/PatchySan,Y
anonymous walk embeddings,https://github.com/nd7141/AWE,Y
graph capsule convolutional neural networks,https://github.com/vermaMachineLearning/Graph-Capsule-CNN-Networks,N
rt-gene: real-time eye gaze estimation in natural environments,https://github.com/Tobias-Fischer/rt_gene,N
fine-tune bert for extractive summarization,https://github.com/nlpyang/BertSum,N
bottom-up abstractive summarization,https://github.com/sebastianGehrmann/bottom-up-summary,N
positional encoding to control output sequence length,https://github.com/takase/control-length,N
abstractive text summarization using sequence-to-sequence rnns and beyond,https://github.com/kashgupta/textsummarization,N
instance-level human parsing via part grouping network,https://github.com/Engineering-Course/CIHP_PGN,N
volumetric and multi-view cnns for object classification on 3d data,https://github.com/charlesq34/3dcnn.torch,N
fpnn: field probing neural networks for 3d data,https://github.com/yangyanli/FPNN,N
learning a hierarchical latent-variable model of 3d shapes,https://github.com/lorenmt/vsl,Y
swiden : convolutional neural networks for depiction invariant object recognition,https://github.com/val-iisc/swiden,N
deep bayesian bandits showdown: an empirical comparison of bayesian deep networks for thompson sampling,https://github.com/tensorflow/models/tree/master/research/deep_contextual_bandits,N
diffusion convolutional recurrent neural network: data-driven traffic forecasting,https://github.com/liyaguang/DCRNN,Y
estimating individual treatment effect: generalization bounds and algorithms,https://github.com/clinicalml/cfrnet,N
model-agnostic meta-learning for fast adaptation of deep networks,https://github.com/jonasrothfuss/promp,N
model-agnostic meta-learning for fast adaptation of deep networks,https://github.com/minseop-aitrics/FewshotLearning,N
model-agnostic meta-learning for fast adaptation of deep networks,https://github.com/yoonholee/MT-net,N
model-agnostic meta-learning for fast adaptation of deep networks,https://github.com/dragen1860/MAML-Pytorch,N
model-agnostic meta-learning for fast adaptation of deep networks,https://github.com/cbfinn/maml_rl,N
model-agnostic meta-learning for fast adaptation of deep networks,https://github.com/dragen1860/MAML-Pytorch-RL,N
model-agnostic meta-learning for fast adaptation of deep networks,https://github.com/katerakelly/pytorch-maml,N
model-agnostic meta-learning for fast adaptation of deep networks,https://github.com/ArnoutDevos/maml-cifar-fs,N
model-agnostic meta-learning for fast adaptation of deep networks,https://github.com/cbfinn/maml,Y
model-agnostic meta-learning for fast adaptation of deep networks,https://github.com/haebeom-lee/maml,N
model-agnostic meta-learning for fast adaptation of deep networks,https://github.com/CocoJam/MAML,N
model-agnostic meta-learning for fast adaptation of deep networks,https://github.com/seppilee/Papers,N
model-agnostic meta-learning for fast adaptation of deep networks,https://github.com/tristandeleu/pytorch-maml-rl,N
model-agnostic meta-learning for fast adaptation of deep networks,https://github.com/mari-linhares/tensorflow-maml,N
model-agnostic meta-learning for fast adaptation of deep networks,https://github.com/yredwood/fewshot_blogpost,N
model-agnostic meta-learning for fast adaptation of deep networks,https://github.com/AntreasAntoniou/HowToTrainYourMAMLPytorch,N
prototypical networks for few-shot learning,https://github.com/joshfp/one-shot-learning,N
prototypical networks for few-shot learning,https://github.com/minseop-aitrics/FewshotLearning,N
prototypical networks for few-shot learning,https://github.com/joshfp/fellowship.ai,N
prototypical networks for few-shot learning,https://github.com/Sha-Lab/FEAT,N
prototypical networks for few-shot learning,https://github.com/ash3n/Prototypical-Network-TF,N
prototypical networks for few-shot learning,https://github.com/jsalbert/prototypical-networks,N
prototypical networks for few-shot learning,https://github.com/cyvius96/prototypical-network-pytorch,N
prototypical networks for few-shot learning,https://github.com/orobix/Prototypical-Networks-for-Few-shot-Learning-PyTorch,N
prototypical networks for few-shot learning,https://github.com/filangel/protonets,N
prototypical networks for few-shot learning,https://github.com/yredwood/fewshot_blogpost,N
prototypical networks for few-shot learning,https://github.com/maxstrobel/HCN-PrototypeLoss-PyTorch,N
prototypical networks for few-shot learning,https://github.com/jakesnell/prototypical-networks,N
prototypical networks for few-shot learning,https://github.com/ash3n/Latent-Similarity,N
prototypical networks for few-shot learning,https://github.com/schatty/prototypical-networks-tf,N
towards a neural statistician,https://github.com/cravingoxygen/neuralstat,N
on first-order meta-learning algorithms,https://github.com/peisungtsai/Reptile-Pytorch-Implementation,N
on first-order meta-learning algorithms,https://github.com/openai/supervised-reptile,N
matching networks for one shot learning,https://github.com/SeonbeomKim/TensorFlow-Matching_Networks_for_One_Shot_Learning,N
matching networks for one shot learning,https://github.com/Pravin71/Fellowship-One-shot-Learning,N
matching networks for one shot learning,https://github.com/welkincici/MatchingNet,N
matching networks for one shot learning,https://github.com/Sha-Lab/FEAT,N
matching networks for one shot learning,https://github.com/AntreasAntoniou/MatchingNetworks,N
matching networks for one shot learning,https://github.com/yijiuzai/Matching-Networks-for-One-Shot-Learning,N
matching networks for one shot learning,https://github.com/facebookresearch/low-shot-shrink-hallucinate,N
matching networks for one shot learning,https://github.com/cnichkawde/MatchingNetwork,N
matching networks for one shot learning,https://github.com/fujenchu/matchingNet,N
matching networks for one shot learning,https://github.com/lwihtittlee/MatchingNetMaster,N
matching networks for one shot learning,https://github.com/knnaraghi/fewshot,Y
matching networks for one shot learning,https://github.com/schatty/matching-networks-tf,N
meta-learning with differentiable convex optimization,https://github.com/kjunelee/MetaOptNet,Y
dynamic few-shot visual learning without forgetting,https://github.com/gidariss/FewShotWithoutForgetting,N
how to train your maml,https://github.com/AntreasAntoniou/HowToTrainYourMAMLPytorch,N
a closer look at few-shot classification,https://github.com/wyharveychen/CloserLookFewShot,N
learning deep parsimonious representations,https://github.com/lrjconan/deep_parsimonious,N
virtual adversarial training: a regularization method for supervised and semi-supervised learning,https://github.com/TOA-ZR/VATcode,N
virtual adversarial training: a regularization method for supervised and semi-supervised learning,https://github.com/takerum/vat_tf,N
virtual adversarial training: a regularization method for supervised and semi-supervised learning,https://github.com/9310gaurav/virtual-adversarial-training,N
virtual adversarial training: a regularization method for supervised and semi-supervised learning,https://github.com/lyakaap/VAT-pytorch,N
mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results,https://github.com/Lan1991Xu/ONE_NeurIPS2018,N
mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results,https://github.com/INK-USC/DualRE,N
mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results,https://github.com/shunk031/chainer-MeanTeachers,N
mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results,https://github.com/benathi/fastswa-semi-sup,N
temporal ensembling for semi-supervised learning,https://github.com/benathi/fastswa-semi-sup,Y
temporal ensembling for semi-supervised learning,https://github.com/Goldesel23/Temporal-Ensembling-for-Semi-Supervised-Learning,N
temporal ensembling for semi-supervised learning,https://github.com/hiram64/temporal-ensembling-semi-supervised,N
temporal ensembling for semi-supervised learning,https://github.com/smlaine2/tempens,N
there are many consistent explanations of unlabeled data: why you should average,https://github.com/benathi/fastswa-semi-sup,Y
semi-supervised learning with ladder networks,https://github.com/fh-seu/HUAWEI-Deep-Learning-Experience,N
semi-supervised learning with ladder networks,https://github.com/jubueche/Convolutional-LadderNet,N
semi-supervised learning with ladder networks,https://github.com/udibr/LRE,Y
semi-supervised learning with ladder networks,https://github.com/arasmus/ladder,Y
semi-supervised learning with ladder networks,https://github.com/brandonrobertz/AcademicUrlTitles,N
semi-supervised learning with ladder networks,https://github.com/CuriousAI/ladder,Y
pairwise confusion for fine-grained visual classification,https://github.com/abhimanyudubey/confusion,Y
autoaugment: learning augmentation policies from data,https://github.com/tensorflow/models/tree/master/research/autoaugment,Y
autoaugment: learning augmentation policies from data,https://github.com/LMaxence/Cifar10_Classification,Y
autoaugment: learning augmentation policies from data,https://github.com/serfaniane/siamese_person_re_id,Y
autoaugment: learning augmentation policies from data,https://github.com/barisozmen/deepaugment,Y
towards faster training of global covariance pooling networks by iterative matrix square root normalization,https://github.com/jiangtaoxie/fast-MPN-COV,N
towards faster training of global covariance pooling networks by iterative matrix square root normalization,https://github.com/osmr/imgclsmob,N
a large-scale car dataset for fine-grained categorization and verification,https://github.com/seanren96/Object-Detection,N
large scale fine-grained categorization and domain-specific transfer learning,https://github.com/richardaecn/cvpr18-inaturalist-transfer,Y
fine-grained representation learning and recognition by exploiting hierarchical semantic embedding,https://github.com/HCPLab-SYSU/HSE,Y
dilated recurrent neural networks,https://github.com/code-terminator/DilatedRNN,Y
full-capacity unitary recurrent neural networks,https://github.com/stwisdom/urnn,N
full-capacity unitary recurrent neural networks,https://github.com/v0lta/Complex-gated-recurrent-neural-networks,N
unitary evolution recurrent neural networks,https://github.com/rand0musername/urnn,N
unitary evolution recurrent neural networks,https://github.com/v0lta/Complex-gated-recurrent-neural-networks,N
unitary evolution recurrent neural networks,https://github.com/Avmb/lowrank-gru,Y
a simple way to initialize recurrent networks of rectified linear units,https://github.com/trevor-richardson/rnn_zoo,Y
inferencing based on unsupervised learning of disentangled representations,https://github.com/tohinz/Bidirectional-InfoGAN,N
adversarial autoencoders,https://github.com/greentfrapp/keras-aae,N
adversarial autoencoders,https://github.com/santi-pdp/pase,N
adversarial autoencoders,https://github.com/eriklindernoren/PyTorch-GAN,N
adversarial autoencoders,https://github.com/LittleLampChen/Keras-PyTorch-GANs,N
adversarial autoencoders,https://github.com/greentfrapp/adversarialautoencoder,N
adversarial autoencoders,https://github.com/MINGUKKANG/Adversarial-AutoEncoder,N
unsupervised and semi-supervised learning with categorical generative adversarial networks,https://github.com/ZhimingZhou/AM-GAN,N
infogan: interpretable representation learning by information maximizing generative adversarial nets,https://github.com/jonasz/progressive_infogan,N
infogan: interpretable representation learning by information maximizing generative adversarial nets,https://github.com/tensorpack/tensorpack,N
infogan: interpretable representation learning by information maximizing generative adversarial nets,https://github.com/SeonbeomKim/TensorFlow-InfoGAN,N
infogan: interpretable representation learning by information maximizing generative adversarial nets,https://github.com/LJSthu/info-GAN,N
infogan: interpretable representation learning by information maximizing generative adversarial nets,https://github.com/buriburisuri/timeseries_gan,N
infogan: interpretable representation learning by information maximizing generative adversarial nets,https://github.com/jeanjerome/semisupervised_timeseries_infogan,N
infogan: interpretable representation learning by information maximizing generative adversarial nets,https://github.com/sidneyp/bidirectional,N
infogan: interpretable representation learning by information maximizing generative adversarial nets,https://github.com/Natsu6767/InfoGAN-PyTorch,N
infogan: interpretable representation learning by information maximizing generative adversarial nets,https://github.com/Murali81/InfoGAN,N
infogan: interpretable representation learning by information maximizing generative adversarial nets,https://github.com/SAGNIKMJR/MetaQNN_ImageGenerationGAN_DiscriminatorFixed_PyTorch,N
infogan: interpretable representation learning by information maximizing generative adversarial nets,https://github.com/rsbandhu/ISIC_2018,N
infogan: interpretable representation learning by information maximizing generative adversarial nets,https://github.com/willwhitney/dc-ign,N
infogan: interpretable representation learning by information maximizing generative adversarial nets,https://github.com/landeros10/infoganJL,N
infogan: interpretable representation learning by information maximizing generative adversarial nets,https://github.com/VitoRazor/Gan_Architecture,N
infogan: interpretable representation learning by information maximizing generative adversarial nets,https://github.com/tensorflow/models/tree/master/research/gan,N
infogan: interpretable representation learning by information maximizing generative adversarial nets,https://github.com/openai/InfoGAN,N
infogan: interpretable representation learning by information maximizing generative adversarial nets,https://github.com/yukia18/pytorch,N
gpipe: efficient training of giant neural networks using pipeline parallelism,https://github.com/tensorflow/lingvo,N
gpipe: efficient training of giant neural networks using pipeline parallelism,https://github.com/alondj/Pytorch-Gpipe,N
squeeze-and-excitation networks,https://github.com/kobiso/CBAM-tensorflow-slim,N
squeeze-and-excitation networks,https://github.com/syiin/human_protein_atlas,Y
squeeze-and-excitation networks,https://github.com/fengjiqiang/pretrainedmodel_pytorch,Y
squeeze-and-excitation networks,https://github.com/TuSimple/neuron-selectivity-transfer,Y
squeeze-and-excitation networks,https://github.com/kobiso/CBAM-keras,N
squeeze-and-excitation networks,https://github.com/moskomule/senet.pytorch,Y
squeeze-and-excitation networks,https://github.com/JHLee0513/Salt_detection_challenge,Y
squeeze-and-excitation networks,https://github.com/secretlyvogon/IndRNNTF,Y
squeeze-and-excitation networks,https://github.com/OliverJBoom/Histopathologic-Cancer-Detection,Y
squeeze-and-excitation networks,https://github.com/ai-med/squeeze_and_excitation,Y
squeeze-and-excitation networks,https://github.com/hujie-frank/SENet,N
squeeze-and-excitation networks,https://github.com/richardaecn/cvpr18-inaturalist-transfer,Y
squeeze-and-excitation networks,https://github.com/varshaneya/Res-SE-Net,Y
squeeze-and-excitation networks,https://github.com/albanie/mcnSENets,Y
squeeze-and-excitation networks,https://github.com/piotrplata/squeeze_excitation_keras,Y
squeeze-and-excitation networks,https://github.com/secretlyvogon/Neural-Network-Implementations,Y
squeeze-and-excitation networks,https://github.com/tangzhenyu/tensorflow_tutorial,Y
squeeze-and-excitation networks,https://github.com/abhi4ssj/squeeze_and_excitation,Y
squeeze-and-excitation networks,https://github.com/osmr/imgclsmob,Y
squeeze-and-excitation networks,https://github.com/exekudos/se-resnet,N
squeeze-and-excitation networks,https://github.com/harshit0511/Deep-Learning,N
squeeze-and-excitation networks,https://github.com/kobiso/CBAM-tensorflow,N
fixup initialization: residual learning without normalization,https://github.com/valilenk/fixup,Y
fixup initialization: residual learning without normalization,https://github.com/Zelgunn/CustomKerasLayers,Y
fixup initialization: residual learning without normalization,https://github.com/hongyi-zhang/Fixup,Y
averaging weights leads to wider optima and better generalization,https://github.com/GeorgOhneH/WerbeSkip,N
averaging weights leads to wider optima and better generalization,https://github.com/wjmaddox/swa_gaussian,Y
averaging weights leads to wider optima and better generalization,https://github.com/benathi/fastswa-semi-sup,Y
averaging weights leads to wider optima and better generalization,https://github.com/kristpapadopoulos/keras_callbacks,Y
averaging weights leads to wider optima and better generalization,https://github.com/timgaripov/swa,Y
averaging weights leads to wider optima and better generalization,https://github.com/kristpapadopoulos/keras-stochastic-weight-averaging,Y
deep pyramidal residual networks,https://github.com/Stick-To/PyramidNet-TF,Y
deep pyramidal residual networks,https://github.com/jhkim89/PyramidNet,Y
deep pyramidal residual networks,https://github.com/osmr/imgclsmob,Y
deep competitive pathway networks,https://github.com/JiaRenChang/CoPaNet,Y
densely connected convolutional networks,https://github.com/jonnor/datascience-master,N
densely connected convolutional networks,https://github.com/agassi4013/Log-DenseNet-Tensorflow,Y
densely connected convolutional networks,https://github.com/modelhub-ai/densenet,Y
densely connected convolutional networks,https://github.com/gaetandi/cheXpert,Y
densely connected convolutional networks,https://github.com/satishjasthi/Densenet_smplified,Y
densely connected convolutional networks,https://github.com/cges60809/DenseNet,Y
densely connected convolutional networks,https://github.com/syiin/ann_cancer_detection,Y
densely connected convolutional networks,https://github.com/wangbinglin1995/tianchi,Y
densely connected convolutional networks,https://github.com/ByakuyaUncia/DenseNetUncia,Y
densely connected convolutional networks,https://github.com/xypan1232/iDeepE,Y
densely connected convolutional networks,https://github.com/fengjiqiang/pretrainedmodel_pytorch,Y
densely connected convolutional networks,https://github.com/eremo2002/Keras-DenseNet,Y
densely connected convolutional networks,https://github.com/Stick-To/DenseNet-TF,Y
densely connected convolutional networks,https://github.com/HLinShan/vision_networks,Y
densely connected convolutional networks,https://github.com/alexz01/proteinatlax,Y
densely connected convolutional networks,https://github.com/minggli/DenseNet,Y
densely connected convolutional networks,https://github.com/YerevaNN/DIIN-in-Keras,Y
densely connected convolutional networks,https://github.com/pudae/tensorflow-densenet,Y
densely connected convolutional networks,https://github.com/nashawnch/ComputerVision-FerrariDetective,Y
densely connected convolutional networks,https://github.com/andreasveit/densenet-pytorch,Y
densely connected convolutional networks,https://github.com/DaikiTanak/manifold_mixup,Y
densely connected convolutional networks,https://github.com/taylor-914/DenseNet,Y
densely connected convolutional networks,https://github.com/0492wzl/tensorflow_slim_densenet,Y
densely connected convolutional networks,https://github.com/EmolLi/Random,N
densely connected convolutional networks,https://github.com/flyyufelix/DenseNet-Keras,Y
densely connected convolutional networks,https://github.com/RaghavHub/rekuten_hack,Y
densely connected convolutional networks,https://github.com/Ze-Yang/vision_networks,Y
densely connected convolutional networks,https://github.com/syiin/resnet_cancer_detection,Y
densely connected convolutional networks,https://github.com/hycis/TensorGraph,Y
densely connected convolutional networks,https://github.com/RajuGudhe/Cancer_Tissue_Detection,Y
densely connected convolutional networks,https://github.com/kshitij0987/cheXpert,N
densely connected convolutional networks,https://github.com/jbernoz/deeppolyp,Y
densely connected convolutional networks,https://github.com/swxhss/densenet_tensorflow,Y
densely connected convolutional networks,https://github.com/bhaskar-gaur/DenseNet-Keras,Y
densely connected convolutional networks,https://github.com/liuzhuang13/DenseNet,Y
densely connected convolutional networks,https://github.com/JielongZ/DenseNets-by-Keras-Implementation,N
densely connected convolutional networks,https://github.com/seasonyc/densenet,Y
densely connected convolutional networks,https://github.com/nashawnch/Computer-Vision-FerrariDetective,Y
densely connected convolutional networks,https://github.com/vaibhawlabs/matchacodes,Y
densely connected convolutional networks,https://github.com/osmr/imgclsmob,Y
densely connected convolutional networks,https://github.com/yichigo/Chest-X-Ray,Y
densely connected convolutional networks,https://github.com/noahfl/densenet-sdr,Y
densely connected convolutional networks,https://github.com/earthLD/Algorithm,Y
densely connected convolutional networks,https://github.com/noahfl/sdr-densenet-pytorch,Y
densely connected convolutional networks,https://github.com/bamos/densenet.pytorch,Y
densely connected convolutional networks,https://github.com/idobronstein/vision_networks,Y
densely connected convolutional networks,https://github.com/Ciprian95/Licenta,Y
densely connected convolutional networks,https://github.com/ApexPredator1/DenseNet_tensorflow,Y
densely connected convolutional networks,https://github.com/ShiyuLiang/odin-pytorch,Y
densely connected convolutional networks,https://github.com/pyaf/DenseNet-MURA-PyTorch,Y
densely connected convolutional networks,https://github.com/titu1994/DenseNet,N
densely connected convolutional networks,https://github.com/lyw1538272605/AnotherTenseNet,Y
densely connected convolutional networks,https://github.com/Anil1331/DenseNet,Y
densely connected convolutional networks,https://github.com/rajkumargithub/densenet.mura,Y
densely connected convolutional networks,https://github.com/muditrastogi/chestai,Y
densely connected convolutional networks,https://github.com/SimJeg/FC-DenseNet,Y
densely connected convolutional networks,https://github.com/liuzhuang13/DenseNetCaffe,Y
densely connected convolutional networks,https://github.com/ikhlestov/vision_networks,Y
densely connected convolutional networks,https://github.com/RRoundTable/OceanLitter_dataset_generator,Y
densely connected convolutional networks,https://github.com/fxfviolet/Densenet_for_quiz_classification,Y
densely connected convolutional networks,https://github.com/asprenger/keras_fc_densenet,Y
densely connected convolutional networks,https://github.com/VinayBN8997/CIFAR-10-object-detection,Y
fractional max-pooling,https://github.com/JonasWechsler/DeepLearningLab5,Y
fractional max-pooling,https://github.com/VladimirGol/KerasFractionalMaxPooling,Y
fractional max-pooling,https://github.com/facebookresearch/SparseConvNet,Y
fractional max-pooling,https://github.com/supercrazysam/Fractional_MaxPooling,Y
towards principled design of deep convolutional networks: introducing simpnet,https://github.com/hexpheus/SimpNet-Tensorflow,N
towards principled design of deep convolutional networks: introducing simpnet,https://github.com/Coderx7/SimpNet,N
towards principled design of deep convolutional networks: introducing simpnet,https://github.com/aligholamee/SimpNet-Tensorflow,N
towards principled design of deep convolutional networks: introducing simpnet,https://github.com/aligholami/SimpNet-Tensorflow,N
wide residual networks,https://github.com/uclaml/Padam,Y
wide residual networks,https://github.com/Xinyi6/CIFAR10-CNN-by-Keras,Y
wide residual networks,https://github.com/LoJunKai/SUTD_initIAtion,N
wide residual networks,https://github.com/lorenmt/mtan,Y
wide residual networks,https://github.com/dalgu90/wrn-tensorflow,Y
wide residual networks,https://github.com/xternalz/WideResNet-pytorch,Y
wide residual networks,https://github.com/meliketoy/wide-resnet.pytorch,N
wide residual networks,https://github.com/tensorflow/models/tree/master/research/autoaugment,Y
wide residual networks,https://github.com/valilenk/fixup,Y
wide residual networks,https://github.com/idobronstein/my_WRN,Y
wide residual networks,https://github.com/aisosalo/CIFAR-10,Y
wide residual networks,https://github.com/szagoruyko/wide-residual-networks,Y
wide residual networks,https://github.com/rishabh135/foodx,Y
wide residual networks,https://github.com/tensorflow/models/tree/master/research/resnet,N
wide residual networks,https://github.com/Stick-To/WideResNet-TF,Y
wide residual networks,https://github.com/osmr/imgclsmob,Y
wide residual networks,https://github.com/liyz15/WideResNet-tensorflow,Y
wide residual networks,https://github.com/thughost2/Padam,Y
wide residual networks,https://github.com/ShiyuLiang/odin-pytorch,Y
wide residual networks,https://github.com/MichaelWangfc/distributed-tensorflow-resnet,N
wide residual networks,https://github.com/c1ph3rr/Wide-Residual-Networks,N
large-scale evolution of image classifiers,https://github.com/marijnvk/LargeScaleEvolution,Y
striving for simplicity: the all convolutional net,https://github.com/mayank-17/Project-Object-Recognition,Y
striving for simplicity: the all convolutional net,https://github.com/JonasWechsler/DeepLearningLab5,Y
striving for simplicity: the all convolutional net,https://github.com/billyshin/CNN-Object-Recognition,Y
striving for simplicity: the all convolutional net,https://github.com/krish-thorcode/Object-Detection,Y
striving for simplicity: the all convolutional net,https://github.com/Aniket84/Object-Recognition-,Y
striving for simplicity: the all convolutional net,https://github.com/Pulkitdzrt/ML-Object-Recognition,Y
striving for simplicity: the all convolutional net,https://github.com/eclique/keras-gradcam,Y
striving for simplicity: the all convolutional net,https://github.com/Shritesh99/Object_Detection_100DaysOfMLCode,Y
striving for simplicity: the all convolutional net,https://github.com/zlenyk/NNCodesAndProjects,Y
striving for simplicity: the all convolutional net,https://github.com/PAIR-code/saliency,Y
striving for simplicity: the all convolutional net,https://github.com/kamata1729/visualize-pytorch,N
striving for simplicity: the all convolutional net,https://github.com/abhasahay/Object-Recognition-using-Deep-Learning,Y
striving for simplicity: the all convolutional net,https://github.com/guillaume-chevalier/Hyperopt-Keras-CNN-CIFAR-100,Y
striving for simplicity: the all convolutional net,https://github.com/Shritesh99/100DaysofMLCodeChallenge,Y
striving for simplicity: the all convolutional net,https://github.com/funalab/PredictMovingDirection,Y
striving for simplicity: the all convolutional net,https://github.com/pricha0912/objectRecognition,Y
striving for simplicity: the all convolutional net,https://github.com/Vooban/Hyperopt-Keras-CNN-CIFAR-100,Y
striving for simplicity: the all convolutional net,https://github.com/kunal-visoulia/Object-Recognition-Using-CNN,Y
striving for simplicity: the all convolutional net,https://github.com/kundajelab/deeplift,Y
striving for simplicity: the all convolutional net,https://github.com/pikinder/nn-patterns,Y
"lets keep it simple, using simple architectures to outperform deeper and more complex architectures",https://github.com/dorlivne/simple_net_pruning,N
"lets keep it simple, using simple architectures to outperform deeper and more complex architectures",https://github.com/luckymouse0/SimpleNet-TF,N
"lets keep it simple, using simple architectures to outperform deeper and more complex architectures",https://github.com/JavierAntoran/moby_dick,N
"lets keep it simple, using simple architectures to outperform deeper and more complex architectures",https://github.com/JavierAntoran/moby_dick_whale_audio_detection,N
"lets keep it simple, using simple architectures to outperform deeper and more complex architectures",https://github.com/Coderx7/SimpleNet,N
"lets keep it simple, using simple architectures to outperform deeper and more complex architectures",https://github.com/Coderx7/SimpleNet_Pytorch,N
"lets keep it simple, using simple architectures to outperform deeper and more complex architectures",https://github.com/JavierAntoran/moby_dick_whale_detection,N
identity mappings in deep residual networks,https://github.com/Stick-To/ResNet-TF,Y
identity mappings in deep residual networks,https://github.com/uclaml/Frank-Wolfe-AdvML,Y
identity mappings in deep residual networks,https://github.com/rickyHong/JPEG-Defense-repl,Y
identity mappings in deep residual networks,https://github.com/Stick-To/resnet,Y
identity mappings in deep residual networks,https://github.com/iArunava/ResNet,Y
identity mappings in deep residual networks,https://github.com/TuSimple/resnet.mxnet,Y
identity mappings in deep residual networks,https://github.com/poloclub/jpeg-defense,Y
identity mappings in deep residual networks,https://github.com/FranckZibi/SharpNet,Y
identity mappings in deep residual networks,https://github.com/wenxinxu/resnet-in-tensorflow,Y
identity mappings in deep residual networks,https://github.com/brain-bzh/MCNN,Y
identity mappings in deep residual networks,https://github.com/tensorflow/models/tree/master/research/resnet,N
identity mappings in deep residual networks,https://github.com/osmr/imgclsmob,Y
identity mappings in deep residual networks,https://github.com/alrojo/lasagne_residual_network,Y
identity mappings in deep residual networks,https://github.com/KaimingHe/resnet-1k-layers,Y
identity mappings in deep residual networks,https://github.com/huangleiBuaa/DecorrelatedBN,Y
identity mappings in deep residual networks,https://github.com/horse007666/ResNet,Y
identity mappings in deep residual networks,https://github.com/MichaelWangfc/distributed-tensorflow-resnet,N
identity mappings in deep residual networks,https://github.com/tensorflow/models/tree/master/research/slim,Y
identity mappings in deep residual networks,https://github.com/junhocho/SRGAN,Y
identity mappings in deep residual networks,https://github.com/hysts/pytorch_resnet_preact,Y
identity mappings in deep residual networks,https://github.com/edufonseca/icassp19,Y
identity mappings in deep residual networks,https://github.com/umich-vl/DecorrelatedBN,Y
deep networks with stochastic depth,https://github.com/shamangary/Pytorch-Stochastic-Depth-Resnet,Y
deep networks with stochastic depth,https://github.com/andreasveit/densenet-pytorch,Y
deep networks with stochastic depth,https://github.com/Stick-To/ResNet-TF,N
deep networks with stochastic depth,https://github.com/dblN/stochastic_depth_keras,Y
deep networks with stochastic depth,https://github.com/EmolLi/Random,N
deep networks with stochastic depth,https://github.com/osmr/imgclsmob,Y
deep networks with stochastic depth,https://github.com/Stick-To/resnet,N
deep networks with stochastic depth,https://github.com/yueatsprograms/Stochastic_Depth,Y
deep networks with stochastic depth,https://github.com/jiweeo/pytorch-stochastic-depth,Y
efficient architecture search by network transformation,https://github.com/han-cai/PathLevel-EAS,Y
deep complex networks,https://github.com/litcoderr/ComplexCNN,Y
deep complex networks,https://github.com/ChihebTrabelsi/deep_complex_networks,Y
all you need is a good init,https://github.com/x5675602/LSUVinit-keras,Y
all you need is a good init,https://github.com/JonasWechsler/DeepLearningLab5,Y
all you need is a good init,https://github.com/ducha-aiki/LSUV-pytorch,Y
all you need is a good init,https://github.com/shunk031/LSUV.pytorch,Y
all you need is a good init,https://github.com/ducha-aiki/LSUVinit,Y
"generalizing pooling functions in convolutional neural networks: mixed, gated, and tree",https://github.com/cypw/DPNs,N
spatially-sparse convolutional neural networks,https://github.com/justinessert/hierarchical-deep-cnn,Y
spatially-sparse convolutional neural networks,https://github.com/simpleintel/jupyter,Y
spatially-sparse convolutional neural networks,https://github.com/facebookresearch/SparseConvNet,Y
scalable bayesian optimization using deep neural networks,https://github.com/robTheBob86/Bayesian-Optimizer,Y
fast and accurate deep network learning by exponential linear units (elus),https://github.com/barrykidney/CarND-Behavioral-Cloning-P3,N
fast and accurate deep network learning by exponential linear units (elus),https://github.com/hughperkins/DeepCL,N
fast and accurate deep network learning by exponential linear units (elus),https://github.com/xiexiexiaoxiexie/Udacity-self-driving-car-engineer-P4-Behavioral-Cloning,N
fast and accurate deep network learning by exponential linear units (elus),https://github.com/chuk-yong/keras-relu-elu-comparison,N
fast and accurate deep network learning by exponential linear units (elus),https://github.com/awur978/Autoencoder,N
fast and accurate deep network learning by exponential linear units (elus),https://github.com/vamsiramakrishnan/BehavioralCloning,N
fast and accurate deep network learning by exponential linear units (elus),https://github.com/AsafBarZvi/Liver_project,N
fast and accurate deep network learning by exponential linear units (elus),https://github.com/chuk-yong/ELU-vs-ReLU-for-Image-Recognition,N
learning activation functions to improve deep neural networks,https://github.com/DivyanshRoy/Learning-Activation-Function-Using-Tensorflow,Y
learning activation functions to improve deep neural networks,https://github.com/ForestAgostinelli/Learned-Activation-Functions-Source,Y
training very deep networks,https://github.com/LiyuanLucasLiu/LM-LSTM-CRF,Y
training very deep networks,https://github.com/yoonkim/lstm-char-cnn,Y
stacked what-where auto-encoders,https://github.com/zhangqinghao0811/unpool,Y
deeply-supervised nets,https://github.com/ellisdg/3DUnetCNN,Y
loss-sensitive generative adversarial networks on lipschitz densities,https://github.com/houze-liu/vanilla-wasserstein-loss-sensitive-dcgan_gradient_penalty,Y
loss-sensitive generative adversarial networks on lipschitz densities,https://github.com/guojunq/lsgan,Y
network in network,https://github.com/modelhub-ai/network-in-network,Y
network in network,https://github.com/abel-leulseged/Quick-Draw,Y
network in network,https://github.com/PowerOfDream/digitx,Y
network in network,https://github.com/pipidog/CNLP,Y
network in network,https://github.com/madhavambati/Face-Recognition,Y
network in network,https://github.com/cchinchristopherj/Right-Whale-Unsupervised-Model,Y
network in network,https://github.com/nagadomi/kaggle-cifar10-torch7,Y
network in network,https://github.com/oktantod/RoboND-DeepLearning-Project,N
network in network,https://github.com/phuijse/MATIC,Y
maxout networks,https://github.com/pmallari/Emotion_Recognition,N
maxout networks,https://github.com/pmallari/Emotion_Recognition_PyTorch,N
maxout networks,https://github.com/philipperemy/tensorflow-maxout,Y
maxout networks,https://github.com/mavenlin/cuda-convnet,Y
empirical evaluation of rectified activations in convolutional network,https://github.com/spinterRu/fashion_mnist,Y
renet: a recurrent neural network based alternative to convolutional networks,https://github.com/SConsul/ReSeg,N
renet: a recurrent neural network based alternative to convolutional networks,https://github.com/fvisin/reseg,N
an analysis of unsupervised pre-training in light of recent advances,https://github.com/ifp-uiuc/anna,Y
stochastic pooling for regularization of deep convolutional neural networks,https://github.com/szagoruyko/imagine-nn,N
improving neural networks by preventing co-adaptation of feature detectors,https://github.com/w510056105/DeepLearning,N
improving neural networks by preventing co-adaptation of feature detectors,https://github.com/DylanMuir/fmin_adam,N
improving neural networks by preventing co-adaptation of feature detectors,https://github.com/dnouri/cuda-convnet,N
improving neural networks by preventing co-adaptation of feature detectors,https://github.com/mdenil/dropout,N
pcanet: a simple deep learning baseline for image classification?,https://github.com/Ldpe2G/PCANet,N
the inaturalist species classification and detection dataset,https://github.com/tensorflow/models/tree/master/research/object_detection,Y
dynamic routing between capsules,https://github.com/Cheng-Lin-Li/SegCaps,Y
dynamic routing between capsules,https://github.com/shashankmanjunath/capsnet,Y
dynamic routing between capsules,https://github.com/laubonghaudoi/CapsNet_guide_PyTorch,Y
dynamic routing between capsules,https://github.com/XifengGuo/CapsNet-Keras,Y
dynamic routing between capsules,https://github.com/dolaram/Deep-Reinforcement-Learning-using-Capsule-Network,Y
dynamic routing between capsules,https://github.com/DanielLongo/CapsNet-PyTorch,N
dynamic routing between capsules,https://github.com/timomernick/pytorch-capsule,Y
dynamic routing between capsules,https://github.com/hli2020/nn_encapsulation,Y
dynamic routing between capsules,https://github.com/Ugenteraan/Capsnet-Tensorflow,N
dynamic routing between capsules,https://github.com/JSerowik/Masters_Thesis,Y
dynamic routing between capsules,https://github.com/naturomics/CapsNet-Tensorflow,Y
dynamic routing between capsules,https://github.com/gram-ai/capsule-networks,Y
dynamic routing between capsules,https://github.com/bhneo/Capsnet-Experiments,Y
dynamic routing between capsules,https://github.com/DanielLongo/CapsGAN,Y
dynamic routing between capsules,https://github.com/khumbuai/kaggle_toxic_comments,Y
dynamic routing between capsules,https://github.com/leekh7411/Capsule-Network-MNIST-Visualization,Y
dynamic routing between capsules,https://github.com/dshelukh/CapsuleLearner,Y
dynamic routing between capsules,https://github.com/sharathsarangmath/Vector-Capsule-Network-for-wild-animal-species-recognition-in-Camera-trap-images,Y
dynamic routing between capsules,https://github.com/akanimax/capsule-network-TensorFlow,Y
dynamic routing between capsules,https://github.com/arsenzaryan/CapsNet,Y
dynamic routing between capsules,https://github.com/Suraj-Panwar/Capsule_Network_based_Deep_Q_learning,Y
dynamic routing between capsules,https://github.com/anastasios-stamoulis/deep-learning-with-csharp-and-cntk,Y
dynamic routing between capsules,https://github.com/im-ant/Capsules-Guys,Y
dynamic routing between capsules,https://github.com/sharathsarangmath/Vector-Capsule-Network-for-wild-animal-species-recognition-in-Camera-trap-images.,Y
dynamic routing between capsules,https://github.com/dolaram/Capsule-Network-for-MNIST-classification,Y
dynamic routing between capsules,https://github.com/mshaikh2/GANs_Comparison,Y
dynamic routing between capsules,https://github.com/Oushesh/CapsClassifigner,Y
res2net: a new multi-scale backbone architecture,https://github.com/fupiao1998/res2net-keras,Y
res2net: a new multi-scale backbone architecture,https://github.com/ChrisMats/Res2Net,Y
res2net: a new multi-scale backbone architecture,https://github.com/lxtGH/OctaveConv_pytorch,Y
res2net: a new multi-scale backbone architecture,https://github.com/yfreedomliTHU/Res2Net,Y
res2net: a new multi-scale backbone architecture,https://github.com/tuanzhangCS/res2net-on-mxnet,Y
hd-cnn: hierarchical deep convolutional neural network for large scale visual recognition,https://github.com/simpleintel/jupyter,N
hd-cnn: hierarchical deep convolutional neural network for large scale visual recognition,https://github.com/justinessert/hierarchical-deep-cnn,N
improved regularization of convolutional neural networks with cutout,https://github.com/LMaxence/Cifar10_Classification,Y
improved regularization of convolutional neural networks with cutout,https://github.com/uoguelph-mlrg/Cutout,Y
improved regularization of convolutional neural networks with cutout,https://github.com/khanrc/pt.fractalnet,Y
improved regularization of convolutional neural networks with cutout,https://github.com/barisozmen/deepaugment,Y
improved regularization of convolutional neural networks with cutout,https://github.com/dishen12/Cuout_modify,Y
semi-supervised learning with context-conditional generative adversarial networks,https://github.com/Nirvan101/Image-Restoration-deep-learning,N
semi-supervised learning with context-conditional generative adversarial networks,https://github.com/mojc/GAN_lesion_filling,N
fractalnet: ultra-deep neural networks without residuals,https://github.com/gustavla/fractalnet,Y
fractalnet: ultra-deep neural networks without residuals,https://github.com/snf/keras-fractalnet,Y
fractalnet: ultra-deep neural networks without residuals,https://github.com/jiye-ML/Classify_FRACTALNET,N
fractalnet: ultra-deep neural networks without residuals,https://github.com/osmr/imgclsmob,Y
multi-digit number recognition from street view imagery using deep convolutional neural networks,https://github.com/schmollgruberja/Machine-Learning-Nanodegree,N
multi-digit number recognition from street view imagery using deep convolutional neural networks,https://github.com/lixilinx/MCMIL,N
multi-digit number recognition from street view imagery using deep convolutional neural networks,https://github.com/mimikaan/Attention-Model,N
multi-digit number recognition from street view imagery using deep convolutional neural networks,https://github.com/beeps82/SVHN_CNN,N
drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution,https://github.com/gan3sh500/octaveconv-pytorch,N
drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution,https://github.com/kk1694/octaveconv-pytorch,N
drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution,https://github.com/vivym/OctaveConv.pytorch,N
drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution,https://github.com/AnjieZheng/OctaveConv-Pytorch,N
drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution,https://github.com/terrychenism/OctaveConv,N
drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution,https://github.com/ddddwee1/Octave-Convolution,N
drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution,https://github.com/l5shi/OCTAVE-CONVOLUTION,N
drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution,https://github.com/iacolippo/octconv-pytorch,N
drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution,https://github.com/xu3kev/octconv-chainer,N
drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution,https://github.com/matsuren/OctaveConv.pytorch,N
drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution,https://github.com/dizhima/octconv,N
drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution,https://github.com/lxtGH/OctaveConv_pytorch,N
drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution,https://github.com/zrl4836/tensorflow_octConv,N
drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution,https://github.com/koshian2/OctConv-TFKeras,N
learning transferable architectures for scalable image recognition,https://github.com/tensorflow/models/tree/master/research/object_detection,Y
learning transferable architectures for scalable image recognition,https://github.com/tensorflow/models/tree/master/research/slim,Y
learning transferable architectures for scalable image recognition,https://github.com/Tingelam/tianchi_gd_defect_pytorch_round1,Y
learning transferable architectures for scalable image recognition,https://github.com/osmr/imgclsmob,Y
learning transferable architectures for scalable image recognition,https://github.com/johannesu/NASNet-keras,Y
learning transferable architectures for scalable image recognition,https://github.com/yurich09/TODO,N
dual path networks,https://github.com/DaikiTanak/manifold_mixup,Y
dual path networks,https://github.com/rwightman/pytorch-dpn-pretrained,Y
dual path networks,https://github.com/cypw/DPNs,Y
dual path networks,https://github.com/fengjiqiang/pretrainedmodel_pytorch,Y
dual path networks,https://github.com/Stick-To/DPN-TF,Y
dual path networks,https://github.com/osmr/imgclsmob,Y
dual path networks,https://github.com/crowdAI/crowdai-musical-genre-recognition-starter-kit,Y
polynet: a pursuit of structural diversity in very deep networks,https://github.com/CUHK-MMLAB/polynet,Y
polynet: a pursuit of structural diversity in very deep networks,https://github.com/osmr/imgclsmob,Y
aggregated residual transformations for deep neural networks,https://github.com/facebookresearch/detectron,N
aggregated residual transformations for deep neural networks,https://github.com/kobiso/CBAM-tensorflow-slim,Y
aggregated residual transformations for deep neural networks,https://github.com/TuSimple/resnet.mxnet,Y
aggregated residual transformations for deep neural networks,https://github.com/D-X-Y/ResNeXt-DenseNet,Y
aggregated residual transformations for deep neural networks,https://github.com/K-Mike/Automatic-salt-deposits-segmentation,Y
aggregated residual transformations for deep neural networks,https://github.com/wsqiankun/resnext,Y
aggregated residual transformations for deep neural networks,https://github.com/Stick-To/ResNeXt-TF,Y
aggregated residual transformations for deep neural networks,https://github.com/JianGoForIt/YellowFin_Pytorch,Y
aggregated residual transformations for deep neural networks,https://github.com/guilherme-pombo/keras_resnext_fpn,Y
aggregated residual transformations for deep neural networks,https://github.com/osmr/imgclsmob,Y
aggregated residual transformations for deep neural networks,https://github.com/facebookresearch/ResNeXt,Y
aggregated residual transformations for deep neural networks,https://github.com/ConnorJL/Dendritic-Spine-Detection,Y
aggregated residual transformations for deep neural networks,https://github.com/prlz77/ResNeXt.pytorch,Y
aggregated residual transformations for deep neural networks,https://github.com/kobiso/CBAM-tensorflow,Y
"inception-v4, inception-resnet and the impact of residual connections on learning",https://github.com/Wenzhima66/readme,N
"inception-v4, inception-resnet and the impact of residual connections on learning",https://github.com/kobiso/CBAM-tensorflow-slim,N
"inception-v4, inception-resnet and the impact of residual connections on learning",https://github.com/Stick-To/Inception-TF,N
"inception-v4, inception-resnet and the impact of residual connections on learning",https://github.com/syzroy/4099-Emotion-Analyser,N
"inception-v4, inception-resnet and the impact of residual connections on learning",https://github.com/Stick-To/Inception,N
"inception-v4, inception-resnet and the impact of residual connections on learning",https://github.com/baldassarreFe/deep-koalarization,N
"inception-v4, inception-resnet and the impact of residual connections on learning",https://github.com/rickyHong/JPEG-Defense-repl,N
"inception-v4, inception-resnet and the impact of residual connections on learning",https://github.com/lazyjek/image_feature_extract_virtualize,N
"inception-v4, inception-resnet and the impact of residual connections on learning",https://github.com/nknytk/ml-study,N
"inception-v4, inception-resnet and the impact of residual connections on learning",https://github.com/poloclub/jpeg-defense,N
"inception-v4, inception-resnet and the impact of residual connections on learning",https://github.com/AKASH2907/bird_species_classification,N
"inception-v4, inception-resnet and the impact of residual connections on learning",https://github.com/Vooban/Hyperopt-Keras-CNN-CIFAR-100,N
"inception-v4, inception-resnet and the impact of residual connections on learning",https://github.com/vudung45/FaceRec,N
"inception-v4, inception-resnet and the impact of residual connections on learning",https://github.com/Dhruvpatel2112/Dhruv-Patel,N
"inception-v4, inception-resnet and the impact of residual connections on learning",https://github.com/hujinxinb/face_detect,N
"inception-v4, inception-resnet and the impact of residual connections on learning",https://github.com/osmr/imgclsmob,N
"inception-v4, inception-resnet and the impact of residual connections on learning",https://github.com/AKASH2907/bird-species-classification,N
"inception-v4, inception-resnet and the impact of residual connections on learning",https://github.com/tensorflow/models/tree/master/research/slim,N
"inception-v4, inception-resnet and the impact of residual connections on learning",https://github.com/Liuyubao/transfer-learning,N
"inception-v4, inception-resnet and the impact of residual connections on learning",https://github.com/guillaume-chevalier/Hyperopt-Keras-CNN-CIFAR-100,N
"inception-v4, inception-resnet and the impact of residual connections on learning",https://github.com/AKASH2907/indian_landmark_recognition,N
"inception-v4, inception-resnet and the impact of residual connections on learning",https://github.com/kobiso/CBAM-tensorflow,N
exploring randomly wired neural networks for image recognition,https://github.com/swdsld/RandWire_tensorflow,Y
exploring randomly wired neural networks for image recognition,https://github.com/JiaminRen/RandWireNN,Y
exploring randomly wired neural networks for image recognition,https://github.com/seungwonpark/RandWireNN,Y
exploring randomly wired neural networks for image recognition,https://github.com/leaderj1001/RandWireNN,Y
exploring randomly wired neural networks for image recognition,https://github.com/timctho/random-wired-nn-tensorflow,Y
xception: deep learning with depthwise separable convolutions,https://github.com/Qinxianshen/tianchi_taobao_2018,Y
xception: deep learning with depthwise separable convolutions,https://github.com/tensorflow/models/tree/master/research/deeplab,Y
xception: deep learning with depthwise separable convolutions,https://github.com/maorshutman/human-protein-atlas-kaggle,Y
xception: deep learning with depthwise separable convolutions,https://github.com/kwotsin/TensorFlow-Xception,Y
xception: deep learning with depthwise separable convolutions,https://github.com/markloyman/LungNoduleRetrieval,Y
xception: deep learning with depthwise separable convolutions,https://github.com/Candice-X/w-net-for-image-segmentation,Y
xception: deep learning with depthwise separable convolutions,https://github.com/LouisFoucard/w-net,Y
xception: deep learning with depthwise separable convolutions,https://github.com/universvm/BacXeption,Y
xception: deep learning with depthwise separable convolutions,https://github.com/osmr/imgclsmob,Y
xception: deep learning with depthwise separable convolutions,https://github.com/modelhub-ai/xception,Y
xception: deep learning with depthwise separable convolutions,https://github.com/crowdAI/crowdai-musical-genre-recognition-starter-kit,Y
xception: deep learning with depthwise separable convolutions,https://github.com/sheldonnnn/exception,Y
rethinking the inception architecture for computer vision,https://github.com/UnofficialJuliaMirror/DeepMark-deepmark,Y
rethinking the inception architecture for computer vision,https://github.com/wbaker01/Bio-Imaging,N
rethinking the inception architecture for computer vision,https://github.com/brandontrabucco/im2txt_express,Y
rethinking the inception architecture for computer vision,https://github.com/uclaml/Frank-Wolfe-AdvML,Y
rethinking the inception architecture for computer vision,https://github.com/Stick-To/Inception-TF,Y
rethinking the inception architecture for computer vision,https://github.com/SophiaYuSophiaYu/ImageCaption,Y
rethinking the inception architecture for computer vision,https://github.com/SimoneCaldarella/facialRecognition,Y
rethinking the inception architecture for computer vision,https://github.com/Stick-To/Inception,Y
rethinking the inception architecture for computer vision,https://github.com/Robinatp/Tensorflow_Model_Inception,Y
rethinking the inception architecture for computer vision,https://github.com/21-projects-for-deep-learning/image2text,Y
rethinking the inception architecture for computer vision,https://github.com/mjhucla/TF-mRNN,Y
rethinking the inception architecture for computer vision,https://github.com/athulyashyju/sample-project,Y
rethinking the inception architecture for computer vision,https://github.com/premthomas/keras-image-classification,N
rethinking the inception architecture for computer vision,https://github.com/Henrylol/Img_Caption,N
rethinking the inception architecture for computer vision,https://github.com/brandontrabucco/im2txt_attend,Y
rethinking the inception architecture for computer vision,https://github.com/AKASH2907/bird_species_classification,Y
rethinking the inception architecture for computer vision,https://github.com/pchimenti/neural_nets,N
rethinking the inception architecture for computer vision,https://github.com/jetrobert/image2text_ShowAndTell,Y
rethinking the inception architecture for computer vision,https://github.com/ChangweiZhang/Awesome-WindowsML-ONNX-Models,N
rethinking the inception architecture for computer vision,https://github.com/r-karthik/Detection-of-pests,Y
rethinking the inception architecture for computer vision,https://github.com/tensorflow/models/tree/master/research/inception,Y
rethinking the inception architecture for computer vision,https://github.com/fchollet/deep-learning-models,Y
rethinking the inception architecture for computer vision,https://github.com/puxinhe/im2txt_v3,Y
rethinking the inception architecture for computer vision,https://github.com/thang86/thang86.github.io,N
rethinking the inception architecture for computer vision,https://github.com/tensorflow/models/tree/master/research/marco,Y
rethinking the inception architecture for computer vision,https://github.com/rai-project/dlframework,N
rethinking the inception architecture for computer vision,https://github.com/UnofficialJuliaMirrorSnapshots/DeepMark-deepmark,Y
rethinking the inception architecture for computer vision,https://github.com/Jayanthipalanichamy/ElucidateTheScene,N
rethinking the inception architecture for computer vision,https://github.com/osmr/imgclsmob,Y
rethinking the inception architecture for computer vision,https://github.com/STB1019/face_recognition,Y
rethinking the inception architecture for computer vision,https://github.com/modelhub-ai/inception-v3,Y
rethinking the inception architecture for computer vision,https://github.com/DeepMark/deepmark,Y
rethinking the inception architecture for computer vision,https://github.com/CESARDELATORRE/ComputerVisionTensorFlowMLNET,Y
rethinking the inception architecture for computer vision,https://github.com/AKASH2907/bird-species-classification,Y
rethinking the inception architecture for computer vision,https://github.com/Ciprian95/Licenta,Y
rethinking the inception architecture for computer vision,https://github.com/kartikperisetla/cherryBlossom,Y
rethinking the inception architecture for computer vision,https://github.com/Bersaelor/AAPLMetalImageRecognition,N
rethinking the inception architecture for computer vision,https://github.com/SimoneCaldarella/faceRecognition,Y
rethinking the inception architecture for computer vision,https://github.com/tensorflow/models/tree/master/research/im2txt,Y
rethinking the inception architecture for computer vision,https://github.com/stoensin/IC,Y
rethinking the inception architecture for computer vision,https://github.com/Urmil28/Medical-Image-Analysis,N
rethinking the inception architecture for computer vision,https://github.com/brandontrabucco/im2txt_match,Y
rethinking the inception architecture for computer vision,https://github.com/tensorflow/models/tree/master/research/slim,Y
rethinking the inception architecture for computer vision,https://github.com/Liuyubao/transfer-learning,Y
rethinking the inception architecture for computer vision,https://github.com/DanishShah/DeepDiagnosis,Y
rethinking the inception architecture for computer vision,https://github.com/AKASH2907/indian_landmark_recognition,Y
rethinking the inception architecture for computer vision,https://github.com/AsafBarZvi/Liver_project,N
batch normalization: accelerating deep network training by reducing internal covariate shift,https://github.com/tensorflow/models/tree/master/research/deeplab,N
batch normalization: accelerating deep network training by reducing internal covariate shift,https://github.com/Xinyi6/CIFAR10-CNN-by-Keras,N
batch normalization: accelerating deep network training by reducing internal covariate shift,https://github.com/KushajveerSingh/SPADE-PyTorch,N
batch normalization: accelerating deep network training by reducing internal covariate shift,https://github.com/Stick-To/Inception,N
batch normalization: accelerating deep network training by reducing internal covariate shift,https://github.com/che9992/Batch_Normalization,N
batch normalization: accelerating deep network training by reducing internal covariate shift,https://github.com/k5iogura/si23tinyyolov2,N
batch normalization: accelerating deep network training by reducing internal covariate shift,https://github.com/simo23/tinyYOLOv2,N
batch normalization: accelerating deep network training by reducing internal covariate shift,https://github.com/lim0606/caffe-googlenet-bn,N
batch normalization: accelerating deep network training by reducing internal covariate shift,https://github.com/ibabbar/Traffic-Sign-Classifier,N
batch normalization: accelerating deep network training by reducing internal covariate shift,https://github.com/LMaxence/Cifar10_Classification,N
batch normalization: accelerating deep network training by reducing internal covariate shift,https://github.com/tangzhenyu/tensorflow_tutorial,N
batch normalization: accelerating deep network training by reducing internal covariate shift,https://github.com/sudhirk999/ResearchPapersLinks,N
batch normalization: accelerating deep network training by reducing internal covariate shift,https://github.com/osmr/imgclsmob,N
batch normalization: accelerating deep network training by reducing internal covariate shift,https://github.com/linghu8812/Convolution-Neural-Network-with-pytorch,N
batch normalization: accelerating deep network training by reducing internal covariate shift,https://github.com/Bersaelor/AAPLMetalImageRecognition,N
batch normalization: accelerating deep network training by reducing internal covariate shift,https://github.com/xiexiexiaoxiexie/Udacity-self-driving-car-engineer-P4-Behavioral-Cloning,N
batch normalization: accelerating deep network training by reducing internal covariate shift,https://github.com/LouisFoucard/StereoConvNet,N
batch normalization: accelerating deep network training by reducing internal covariate shift,https://github.com/koolhussain/Self-Driving-Car,N
batch normalization: accelerating deep network training by reducing internal covariate shift,https://github.com/maxpastor/tensorflow-fevrier,N
batch normalization: accelerating deep network training by reducing internal covariate shift,https://github.com/tensorflow/models/tree/master/research/slim,N
batch normalization: accelerating deep network training by reducing internal covariate shift,https://github.com/Liuyubao/transfer-learning,N
batch normalization: accelerating deep network training by reducing internal covariate shift,https://github.com/utsawk/CarND-Traffic-Sign-Classifier-Project,N
batch normalization: accelerating deep network training by reducing internal covariate shift,https://github.com/harshit0511/Deep-Learning,N
batch normalization: accelerating deep network training by reducing internal covariate shift,https://github.com/gmouzella/Efective_TensorFlow,N
shufflenet: an extremely efficient convolutional neural network for mobile devices,https://github.com/tensorpack/tensorpack,N
shufflenet: an extremely efficient convolutional neural network for mobile devices,https://github.com/afzalahmad0203/Tensorflow-Shufflenet,N
shufflenet: an extremely efficient convolutional neural network for mobile devices,https://github.com/afzalahmad0203/Numpy-Shufflenet,N
shufflenet: an extremely efficient convolutional neural network for mobile devices,https://github.com/minhto2802/keras-shufflenet,N
shufflenet: an extremely efficient convolutional neural network for mobile devices,https://github.com/europa1610/Tensorflow-Shufflenet,N
shufflenet: an extremely efficient convolutional neural network for mobile devices,https://github.com/clavichord93/MENet,N
shufflenet: an extremely efficient convolutional neural network for mobile devices,https://github.com/osmr/imgclsmob,N
shufflenet: an extremely efficient convolutional neural network for mobile devices,https://github.com/MrRen-sdhm/Embedded_Multi_Object_Detection_CNN,N
shufflenet: an extremely efficient convolutional neural network for mobile devices,https://github.com/afzalahmad0203/tf_shufflenet,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/Tsejing/object_detection,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/jooyounghun/Separable-CNN,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/stanford-cs348k/asst2-mobilenet,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/Edmonton-School-of-AI/ml5-Simple-Image-Classification,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/quanchun/falcon-exp,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/BIG-CHENG/FaceRec,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/CyFeng16/MobileNetV2-tf.keras,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/liqiang2018/Vehicle_delector,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/zhuyi55/week9,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/pessimiss/ai100-w8-master,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/RealHulubulu/openCVObjectDetectionRPi,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/BillyGun27/mobilenet_model,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/suryapa1/MAX-Object-Detector-v1,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/ruinmessi/RFBNet,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/cftang0827/human_recognition,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/sokeeffe/caffe_yolo,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/MohammedAbdou285/CarND_Term3_Project3_Capstone,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/ildoonet/tf-pose-estimation,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/falcon-snu/FALCON,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/0xPr0xy/MobileNet-CoreML,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/JagadishSivakumar/Image-Classifier-ml5.js,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/ailohc/2018-CS496-1st-Assignment,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/sufeidechabei/gluon-mobilenet-yolov3,Y
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/TTMRonald/MobileNet_Zoo,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/ctuning/ck-mlperf,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/cfdoge/w9ai,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/SyBorg91/pose-estimation-detection,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/IBM/MAX-Object-Detector,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/ice-melt/csdn-w9,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/0xPr0xy/CoreML,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/eremo2002/Keras-MobileNet,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/maituduy/Mxnet_MobileNet,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/Zehaos/MobileNet,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/osmr/imgclsmob,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/liqiang2018/ssd_mobilenet1.5,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/titu1994/MobileNetworks,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/jiawulu/ai-w8,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/eftPavel/Hack-Aubg-2018,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/DCASE-REPO/dcase2019_task2_baseline,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/shicai/MobileNet-Caffe,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/quanchun/FALCON,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/yanbeic/Deep-Association-Learning,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/ac-optimus/ESDC_IntelCup2018,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/cftang0827/pedestrian_recognition,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/tensorflow/models/tree/master/research/slim,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/szagoruyko/pyinn,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/TurtleGo/project-vehicle-detect,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/MrRen-sdhm/Embedded_Multi_Object_Detection_CNN,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/Zoushuang86/quiz_w8,N
mobilenets: efficient convolutional neural networks for mobile vision applications,https://github.com/etneuhct/eyevoice,N
explaining and harnessing adversarial examples,https://github.com/sdemyanov/ConvNet,Y
explaining and harnessing adversarial examples,https://github.com/tensorflow/cleverhans,Y
explaining and harnessing adversarial examples,https://github.com/facebookresearch/adversarial_image_defenses,Y
explaining and harnessing adversarial examples,https://github.com/johnsonkee/graduate_design,Y
explaining and harnessing adversarial examples,https://github.com/bingcheng45/hnr-extension,Y
explaining and harnessing adversarial examples,https://github.com/jrguo/FastGradientSignMNIST,Y
explaining and harnessing adversarial examples,https://github.com/1Konny/FGSM,Y
explaining and harnessing adversarial examples,https://github.com/KeAWang/BayesianGAN4AdversarialAttacks,Y
explaining and harnessing adversarial examples,https://github.com/iirishikaii/cleverhans,Y
explaining and harnessing adversarial examples,https://github.com/utkuozbulak/pytorch-cnn-adversarial-attacks,Y
explaining and harnessing adversarial examples,https://github.com/LawrenceMMStewart/Adversarial_Attack,Y
explaining and harnessing adversarial examples,https://github.com/eth-sri/diffai,Y
explaining and harnessing adversarial examples,https://github.com/cfinlay/tulip,N
explaining and harnessing adversarial examples,https://github.com/drewbarot/Un-CNN,Y
explaining and harnessing adversarial examples,https://github.com/alexmlamb/verifiable_robustness,Y
explaining and harnessing adversarial examples,https://github.com/AngusG/cleverhans-attacking-bnns,Y
explaining and harnessing adversarial examples,https://github.com/amerch/CIFAR100-Training,Y
explaining and harnessing adversarial examples,https://github.com/openai/cleverhans,Y
explaining and harnessing adversarial examples,https://github.com/HowToMakeABomb101/Hot2MakeAB0mbSite,Y
explaining and harnessing adversarial examples,https://github.com/alfrei/ml_blitz_2018,Y
explaining and harnessing adversarial examples,https://github.com/locuslab/convex_adversarial,Y
margin-based parallel corpus mining with multilingual sentence embeddings,https://github.com/facebookresearch/LASER,N
margin-based parallel corpus mining with multilingual sentence embeddings,https://github.com/OriginPower/LASER,N
margin-based parallel corpus mining with multilingual sentence embeddings,https://github.com/imamathcat/LASER_Dependencies,N
margin-based parallel corpus mining with multilingual sentence embeddings,https://github.com/LawrenceDuan/myLASER,N
graph2seq: graph to sequence learning with attention-based neural networks,https://github.com/IBM/Graph2Seq,N
graph2seq: graph to sequence learning with attention-based neural networks,https://github.com/ReleasedBrainiac/GraphToSequenceNN,N
graph2seq: graph to sequence learning with attention-based neural networks,https://github.com/RandolphVI/Graph2Seq,N
face attention network: an effective face detector for the occluded faces,https://github.com/yvette-suyu/Face-Detection-intelligent-city-2018,N
adversarial occlusion-aware face detection,https://github.com/IssacCyj/Adversarial-Occlussion-aware-Face-Detection,N
disguisenet : a contrastive approach for disguised face verification in the wild,https://github.com/pvskand/DisguiseNet,N
lprnet: license plate recognition via deep neural networks,https://github.com/lyl8213/Plate_Recognition-LPRnet,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/QueensGambit/CrazyAra,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/l1t1/lz19,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/alreadydone/Leela_Phoenix,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/jedz5/hisFirstRepo,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/jonahs99/baby-zero,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/plkmo/AlphaZero_Chess,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/qinwang/leela-zero,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/cjohnchen/changebale,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/gcp/leela-zero,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/cjohnchen/roy7,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/Zeta36/chess-alpha-zero,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/John-Yu/leelaApplication,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/tensorflow/minigo,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/leela-zero/leela-zero,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/gtagency/beta-zero,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/zhoujianxing123/minigo_v17,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/ryanp73/ChessAI,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/svikramank/chess-deepRL,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/sai-dev/sai,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/MingzhenY/Projects,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/simoll/apo,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/plkmo/AlphaZero_Connect4,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/pathway/alphaxos,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/jasonrobwebster/alphazero-clone,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/samhippie/q-game-agent,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/Neo-The1/ThinkingTicTacToe,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/alan1113/batch,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/kennychenfs/leela-zero9,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/saikrishna-1996/deep_pepper_chess,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/mengyangbai/CchessGo,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/cjohnchen/mirror,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/plkmo/AlphaGoZero_Chess,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/kennychenfs/lzcpu,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/davidokao/beta-zero,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/cjohnchen/sai,N
mastering chess and shogi by self-play with a general reinforcement learning algorithm,https://github.com/fantianwen/leela13_training,N
understanding humans in crowded scenes: deep nested adversarial learning and a new benchmark for multi-human parsing,https://github.com/ZhaoJ9014/Multi-Human-Parsing,N
understanding humans in crowded scenes: deep nested adversarial learning and a new benchmark for multi-human parsing,https://github.com/ZhaoJ9014/Multi-Human-Parsing_MHP,N
multiple-human parsing in the wild,https://github.com/ZhaoJ9014/Multi-Human-Parsing,N
multiple-human parsing in the wild,https://github.com/ZhaoJ9014/Multi-Human-Parsing_MHP,N
instance-aware semantic segmentation via multi-task network cascades,https://github.com/daijifeng001/MNC,N
structural neural encoders for amr-to-text generation,https://github.com/mdtux89/OpenNMT-py,Y
structural neural encoders for amr-to-text generation,https://github.com/mdtux89/OpenNMT-py-AMR-to-text,Y
passage re-ranking with bert,https://github.com/sam3oh5/sam-duchamp,N
passage re-ranking with bert,https://github.com/gsgoncalves/bert4trec,Y
passage re-ranking with bert,https://github.com/nyu-dl/dl4marco-bert,Y
an updated duet model for passage re-ranking,https://github.com/dfcf93/MSMARCO,N
context based approach for second language acquisition,https://github.com/iampuntre/slam18,Y
context based approach for second language acquisition,https://github.com/arjun-rao/slam18,Y
graphite: iterative generative modeling of graphs,https://github.com/ermongroup/graphite,Y
deep graph infomax,https://github.com/PetarV-/DGI,Y
learning to make predictions on graphs with autoencoders,https://github.com/vuptran/graph-representation-learning,Y
convolutional neural networks on graphs with fast localized spectral filtering,https://github.com/SwissDataScienceCenter/DeepSphere,N
convolutional neural networks on graphs with fast localized spectral filtering,https://github.com/tkipf/gcn,N
convolutional neural networks on graphs with fast localized spectral filtering,https://github.com/rishabhguptagithub/graph_convnet_retina,N
convolutional neural networks on graphs with fast localized spectral filtering,https://github.com/DebagMASTA/test2,N
convolutional neural networks on graphs with fast localized spectral filtering,https://github.com/mdeff/cnn_graph,Y
convolutional neural networks on graphs with fast localized spectral filtering,https://github.com/sheryl-ai/MemGCN,N
convolutional neural networks on graphs with fast localized spectral filtering,https://github.com/sheryl-ai/MVGCN,N
convolutional neural networks on graphs with fast localized spectral filtering,https://github.com/Fuhongshuai/FUHS_GraphCNN,Y
convolutional neural networks on graphs with fast localized spectral filtering,https://github.com/zdcuob/cnn_graph-,Y
convolutional neural networks on graphs with fast localized spectral filtering,https://github.com/brain-bzh/MCNN,N
convolutional neural networks on graphs with fast localized spectral filtering,https://github.com/sk1712/gcn_metric_learning,N
convolutional neural networks on graphs with fast localized spectral filtering,https://github.com/xbresson/spectral_graph_convnets,N
graphgan: graph representation learning with generative adversarial nets,https://github.com/hwwang55/GraphGAN,Y
struc2vec: learning node representations from structural identity,https://github.com/leoribeiro/struc2vec,N
struc2vec: learning node representations from structural identity,https://github.com/shenweichen/GraphEmbedding,Y
node2vec: scalable feature learning for networks,https://github.com/eliorc/node2vec,Y
node2vec: scalable feature learning for networks,https://github.com/shenweichen/GraphEmbedding,N
line: large-scale information network embedding,https://github.com/leihuayi/NetworkEmbedding,Y
line: large-scale information network embedding,https://github.com/shenweichen/GraphEmbedding,Y
line: large-scale information network embedding,https://github.com/tangjianpku/LINE,N
evaluating surgical skills from kinematic data using convolutional neural networks,https://github.com/hfawaz/miccai18,Y
pose2seg: detection free human instance segmentation,https://github.com/liruilong940607/Pose2Seg,N
pose2seg: detection free human instance segmentation,https://github.com/liruilong940607/OCHumanApi,N
yolact: real-time instance segmentation,https://github.com/dbolya/yolact,Y
mask scoring r-cnn,https://github.com/zjhuang22/maskscoring_rcnn,Y
early hospital mortality prediction using vital signals,https://github.com/RezaSadeghiWSU/Early-Hospital-Mortality-Prediction-using-Vital-Signals,Y
predicting city safety perception based on visual image content,https://github.com/sergiojx/uspBogota2018,N
xdeepfm: combining explicit and implicit feature interactions for recommender systems,https://github.com/Rearcher/ICME2019_Short_Video_Understanding_Challenge_Rank14,N
xdeepfm: combining explicit and implicit feature interactions for recommender systems,https://github.com/Leavingseason/xDeepFM,N
xdeepfm: combining explicit and implicit feature interactions for recommender systems,https://github.com/shenweichen/DeepCTR,N
deepfm: a factorization-machine based neural network for ctr prediction,https://github.com/Leavingseason/OpenLearning4DeepRecsys,Y
deepfm: a factorization-machine based neural network for ctr prediction,https://github.com/Sushilkisu/strata-conference-ca-2018-master,N
deepfm: a factorization-machine based neural network for ctr prediction,https://github.com/xxxmin/ctr_Keras,Y
deepfm: a factorization-machine based neural network for ctr prediction,https://github.com/shenweichen/DeepCTR,N
product-based neural networks for user response prediction,https://github.com/Atomu2014/product-nets,Y
product-based neural networks for user response prediction,https://github.com/xxxmin/ctr_Keras,Y
product-based neural networks for user response prediction,https://github.com/Atomu2014/product-nets-distributed,Y
product-based neural networks for user response prediction,https://github.com/shenweichen/DeepCTR,Y
wide & deep learning for recommender systems,https://github.com/floraxhuang/Movie-Recommendation-System,Y
wide & deep learning for recommender systems,https://github.com/shenweichen/DeepCTR,Y
wide & deep learning for recommender systems,https://github.com/Sushilkisu/strata-conference-ca-2018-master,N
wide & deep learning for recommender systems,https://github.com/xxxmin/ctr_Keras,Y
wide & deep learning for recommender systems,https://github.com/pollyyu/Final_Project_MachineLearning_in_TensorFlow_Berkeley,Y
wide & deep learning for recommender systems,https://github.com/deepakshankar94/Exploring-DL-based-Recommendation-Systems,Y
wide & deep learning for recommender systems,https://github.com/deepakshankar94/Movie-Recommendation-System,Y
deep learning over multi-field categorical data: a case study on user response prediction,https://github.com/ddatta-DAC/Learning,N
deep learning over multi-field categorical data: a case study on user response prediction,https://github.com/wnzhang/deep-ctr,N
deep learning over multi-field categorical data: a case study on user response prediction,https://github.com/shenweichen/DeepCTR,N
deep interest network for click-through rate prediction,https://github.com/YafeiWu/DIEN,Y
deep interest network for click-through rate prediction,https://github.com/johnlevi/recsys,Y
deep interest network for click-through rate prediction,https://github.com/zhougr1993/DeepInterestNetwork,Y
deep interest network for click-through rate prediction,https://github.com/shenweichen/DeepCTR,Y
learning attraction field representation for robust line segment detection,https://github.com/cherubicXN/afm_cvpr2019,Y
identifying well-formed natural language questions,https://github.com/google-research-datasets/query-wellformedness,Y
hardness-aware deep metric learning,https://github.com/wzzheng/HDML,Y
"a corpus with multi-level annotations of patients, interventions and outcomes to support language processing for medical literature",https://github.com/maxaalexeeva/PICO,N
"a corpus with multi-level annotations of patients, interventions and outcomes to support language processing for medical literature",https://github.com/devkotasabin/EBM-NLP,N
multi-view to novel view: synthesizing novel views with self-learned confidence,https://github.com/shaohua0116/Multiview2Novelview,Y
dgc-net: dense geometric correspondence network,https://github.com/AaltoVision/DGC-Net,Y
"pwc-net: cnns for optical flow using pyramid, warping, and cost volume",https://github.com/rickyHong/tfoptflow-repl,Y
"pwc-net: cnns for optical flow using pyramid, warping, and cost volume",https://github.com/RanhaoKang/PWC-Net_pytorch,Y
"pwc-net: cnns for optical flow using pyramid, warping, and cost volume",https://github.com/NVIDIA/flownet2-pytorch,Y
"pwc-net: cnns for optical flow using pyramid, warping, and cost volume",https://github.com/daigo0927/pwcnet,Y
"pwc-net: cnns for optical flow using pyramid, warping, and cost volume",https://github.com/philferriere/tfoptflow,Y
"pwc-net: cnns for optical flow using pyramid, warping, and cost volume",https://github.com/yanqi1811/PWC-Net,Y
"pwc-net: cnns for optical flow using pyramid, warping, and cost volume",https://github.com/daigo0927/PWC-Net_tf,Y
"pwc-net: cnns for optical flow using pyramid, warping, and cost volume",https://github.com/MurrayC7/PWC-Net,Y
"pwc-net: cnns for optical flow using pyramid, warping, and cost volume",https://github.com/NVlabs/PWC-Net,Y
flownet 2.0: evolution of optical flow estimation with deep networks,https://github.com/ElliotHYLee/VisualOdometry3D,N
flownet 2.0: evolution of optical flow estimation with deep networks,https://github.com/rickyHong/tfoptflow-repl,Y
flownet 2.0: evolution of optical flow estimation with deep networks,https://github.com/NVIDIA/flownet2-pytorch,Y
flownet 2.0: evolution of optical flow estimation with deep networks,https://github.com/philferriere/tfoptflow,Y
a repository of conversational datasets,https://github.com/qinguangjun/conversational-datasets,Y
a repository of conversational datasets,https://github.com/PolyAI-LDN/conversational-datasets,Y