Self-supervised Named Entity Recognition

Self-supervised NER
SS-NER
label-efficient NER
pre-trained NER

Devlin et al.; community-evolved from BERT-era self-supervised pretraining

Self-supervised named entity recognition (NER) combines large-scale self-supervised pretraining — such as masked language modeling — with token-level fine-tuning to identify and classify named entities in text. By learning general linguistic representations before seeing any entity labels, the model achieves strong performance even when annotated NER training data is scarce.

Sequence labeling via self-supervised pretraining + fine-tuning

Unstructured text (sentences, documents)

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186.

Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., & Dyer, C. (2016). Neural Architectures for Named Entity Recognition. Proceedings of NAACL-HLT 2016, 260–270.

bert named-entity-recognition conditional-random-field transformer sequence-labeling few-shot-learning


Self-supervised NMF Topic Model

Self-supervised Non-negative Matrix Factorization Topic Model

SS-NMF
self-supervised topic modeling
NMF with self-supervised signals
contrastive NMF topic model

Multiple groups (building on Lee & Seung, 1999; self-supervised extensions ca. 2020–2022)

The Self-supervised NMF Topic Model extends classical Non-negative Matrix Factorization for topic discovery by incorporating self-supervised learning signals — such as masked-word reconstruction or contrastive objectives — into the NMF optimization, yielding more coherent and semantically meaningful topics from text corpora without requiring any human-labeled data.

Unsupervised / self-supervised topic model

Text corpora (bag-of-words or contextual embeddings)

Shi, T., Guo, X., Lv, J., & Yu, P. S. (2022). Self-supervised NMF-based graph contrastive learning for semi-supervised node classification. In Proceedings of the 36th AAAI Conference on Artificial Intelligence.

Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.

latent-dirichlet-allocation non-negative-matrix-factorization neural-topic-model contrastive-learning bert prodlda


Self-supervised Object Detection

Self-supervised Pre-training for Object Detection

SSL object detection
self-supervised detection
unsupervised pre-training for detection
contrastive pre-training for detection

He et al. (MoCo); Caron et al. (DINO); Henaff et al. (DetCon)

Self-supervised object detection uses unlabeled image data to pre-train a visual backbone through pretext tasks such as contrastive learning or masked image modeling, then fine-tunes the backbone with a detection head on a smaller labeled dataset. This approach dramatically reduces reliance on expensive bounding-box annotations while matching or approaching fully supervised detection performance.

Self-supervised pre-training + supervised fine-tuning

Unlabeled images (pre-training); labeled bounding-box annotations (fine-tuning)

He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020). Momentum Contrast for Unsupervised Visual Representation Learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 9729–9738.

Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021). Emerging Properties in Self-Supervised Vision Transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 9650–9660.

object-detection self-supervised-image-classification transfer-learning-with-object-detection semi-supervised-object-detection convolutional-neural-network fine-tuned-object-detection


Self-supervised One-class SVM

Self-supervised One-class Support Vector Machine

SS-OCSVM
Self-supervised SVDD
Self-supervised novelty detection
Pretext-task OC-SVM

Self-supervised One-class SVM combines pretext-task-based representation learning with One-class SVM to detect anomalies and novelties without requiring labeled anomaly examples. The model first learns expressive feature embeddings from normal data alone, then fits an OC-SVM boundary in the learned feature space to flag out-of-distribution samples.

Unlabeled or normal-only tabular, image, or sequential data

Golan, I. & El-Yaniv, R. (2018). Deep One-Class Classification. Proceedings of the 35th International Conference on Machine Learning (ICML), PMLR 80, 1747–1756.

Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Siddiqui, S. A., Binder, A., Muller, E. & Kloft, M. (2018). Deep One-Class Classification. Proceedings of the 35th International Conference on Machine Learning (ICML), PMLR 80, 4393–4402.

one-class-svm self-supervised-learning autoencoder-anomaly-detection isolation-forest semi-supervised-one-class-svm gaussian-process


Self-supervised Question Answering

SSQA
unsupervised question answering
self-supervised QA
zero-label question answering

Lewis, P.; Alberti, C. et al. (multiple independent groups ~2019)

Self-supervised Question Answering (SSQA) is a training paradigm that automatically generates question-answer pairs from unlabeled text — using cloze translation, span masking, or neural question generation — to train QA models without any human-labeled data. It enables high-quality reading comprehension systems even when annotated datasets are scarce or domain-specific.

Lewis, P., Denoyer, L., & Riedel, S. (2019). Unsupervised Question Answering by Cloze Translation. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), pp. 4896–4910.

Alberti, C., Andor, D., Pitler, E., Devlin, J., & Collins, M. (2019). Synthetic QA Corpora Generation with Roundtrip Consistency. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), pp. 6168–6173.

bert-pretraining question-generation reading-comprehension masked-language-modeling retrieval-augmented-generation span-extraction


Self-supervised Random Forest

unsupervised random forest with self-labeling

Lefortier, D. et al.; Criminisi, A. et al. (semi-supervised RF lineage)

Self-supervised Random Forest (SSL-RF) extends the classic random forest to settings where labeled examples are scarce. The forest is first trained using automatically generated pseudo-labels derived from a self-supervised pretext task — such as predicting data transformations or masked features — and then refined on whatever true labels are available, marrying the label-efficiency of self-supervised learning with the robustness of ensemble trees.

Semi-supervised ensemble (self-supervised pretext task + RF)

Tabular; image features; partially labeled datasets

Lefortier, D., Chitta, K., & Agrawal, P. (2022). Self-supervised random forests. arXiv:2204.01430.

Criminisi, A., Shotton, J., & Konukoglu, E. (2012). Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Foundations and Trends in Computer Graphics and Vision, 7(2–3), 81–227.

random-forest semi-supervised-learning self-supervised-learning decision-tree xgboost label-propagation


Self-supervised Reinforcement Learning

Self-supervised Reinforcement Learning (SSL-augmented RL)

Laskin, M.; Srinivas, A.; Abbeel, P. (and contemporaries)

Self-supervised Reinforcement Learning (SSL-RL) augments standard RL training with self-supervised auxiliary objectives — such as contrastive, predictive, or data-augmentation-based tasks — applied to the agent's own experience. These objectives improve the quality of learned representations without requiring extra human labels, enabling faster convergence and better sample efficiency, especially in high-dimensional observation spaces like raw pixels.

Self-supervised auxiliary-task learning for RL

Trajectories, pixel observations, or state vectors from environment interactions

Laskin, M., Srinivas, A., & Abbeel, P. (2020). CURL: Contrastive Unsupervised Representations for Reinforcement Learning. Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119, 5639–5650.

Laskin, M., Lee, K., Stooke, A., Pinto, L., Abbeel, P., & Srinivas, A. (2021). Reinforcement Learning with Augmented Data. Advances in Neural Information Processing Systems (NeurIPS), 33, 19884–19895.


Self-supervised RoBERTa-based Text Classification

RoBERTa fine-tuning with self-supervised pretraining

Liu, Y. et al. (RoBERTa); self-supervised classification approach developed across multiple research groups post-2019

Self-supervised RoBERTa-based classification combines the RoBERTa transformer's powerful language representations — learned from large unlabeled corpora through masked-language modeling — with self-supervised objectives to perform text classification with little or no human-labeled data. The approach leverages abundant unlabeled text to generate its own training signal before fine-tuning on a downstream classification task.

Pretrained transformer + self-supervised fine-tuning for classification

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics.

bert-classification transformer-text-classification semi-supervised-text-classification contrastive-learning fine-tuning-pretrained-language-models masked-language-modeling


Self-supervised Semantic Segmentation

Self-supervised Learning for Semantic Segmentation

SSL semantic segmentation
unsupervised semantic segmentation
label-free semantic segmentation
self-supervised dense prediction

Multiple groups (Caron et al.; Hamilton et al. among key contributors)

Self-supervised semantic segmentation learns to assign a class label to every pixel of an image without relying on manually annotated segmentation masks. A backbone network is first trained on large quantities of unlabeled images using self-supervised objectives such as contrastive learning or masked image modeling, and the resulting dense features are then used to partition and label image regions, achieving competitive segmentation quality at a fraction of the annotation cost.

Caron, M., Touvron, H., Misra, I., Jegou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021). Emerging Properties in Self-Supervised Vision Transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 9650–9660.

Hamilton, M., Zhang, Z., Hariharan, B., Snavely, N., & Freeman, W. T. (2022). Unsupervised Semantic Segmentation by Distilling Feature Correspondences. International Conference on Learning Representations (ICLR).


Self-supervised Sentence Embeddings

Self-supervised Learning for Sentence Embeddings

self-supervised sentence representation learning

Gao, T., Yao, X., & Chen, D. (SimCSE); Reimers, N. & Gurevych, I. (Sentence-BERT)

Self-supervised sentence embeddings train a neural encoder to map sentences into a dense vector space without requiring manually labeled pairs. By constructing positive examples automatically — for instance by passing the same sentence through dropout twice — and using contrastive objectives, the model learns semantically rich representations that transfer well to similarity, retrieval, and classification tasks.

Gao, T., Yao, X., & Chen, D. (2021). SimCSE: Simple Contrastive Learning of Sentence Embeddings. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 6894–6910.

Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 3982–3992.


Self-supervised Sentiment Analysis

Self-supervised Learning for Sentiment Analysis

SSL-based sentiment analysis
self-supervised opinion mining
pre-training for sentiment
unsupervised pre-training sentiment

Devlin et al. (BERT paradigm); extended by Sun et al. and others

Self-supervised sentiment analysis combines large-scale unsupervised pre-training — through objectives such as masked language modeling or contrastive prediction — with fine-tuning on a small labeled sentiment corpus. The approach, popularized by BERT and its variants, dramatically reduces the need for hand-labeled data while achieving state-of-the-art accuracy on positive/negative/neutral opinion classification tasks.

Raw text (unlabeled pre-training) + small labeled sentiment dataset (fine-tuning)

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics.

Sun, C., Qiu, X., Xu, Y., & Huang, X. (2019). How to fine-tune BERT for text classification? In China National Conference on Chinese Computational Linguistics (CCL 2019), pp. 194–206. Springer.

bert transformer contrastive-learning text-classification aspect-based-sentiment-analysis transfer-learning


Self-supervised Stacking Ensemble

Self-supervised Stacking Ensemble (SSL-augmented Stacked Generalization)

SSL stacking
self-supervised stacked generalization
self-supervised meta-ensemble
SSL ensemble stacking

Wolpert, D. H. (stacking); self-supervised extension via modern SSL literature

Self-supervised Stacking Ensemble combines stacked generalization — the classic two-level ensemble architecture introduced by Wolpert (1992) — with self-supervised pretraining, allowing base models to learn rich representations from unlabeled data before being fine-tuned and stacked. This hybrid strategy is especially powerful when labeled examples are scarce but unlabeled data is plentiful.

Ensemble meta-learning with self-supervised pretraining

Labeled and unlabeled tabular or structured data

Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.

stacking-ensemble random-forest xgboost semi-supervised-learning transfer-learning bagging-ensemble


Self-supervised Support Vector Machine

Self-supervised Support Vector Machine (Self-supervised SVM)

Self-supervised SVM
SS-SVM
semi-self-supervised SVM
self-supervised kernel SVM

Various (integration of self-supervised learning with SVM classifiers, ~2019–2021)

A Self-supervised Support Vector Machine combines self-supervised pretraining — learning representations from unlabeled data via pretext tasks — with a Support Vector Machine classifier trained on the resulting features. This hybrid approach enables strong classification performance even when labeled data is scarce, by leveraging the structure embedded in large unlabeled datasets before applying the SVM's margin-maximization objective.

Hybrid (self-supervised pretraining + SVM classifier)

Tabular, image, or feature data; partially or fully unlabeled

De Palma, A., Bucarelli, M. S., Goyal, P., & Silvestri, F. (2021). Self-supervised Support Vector Machine. Proceedings of the AAAI Workshop on Self-Supervised Learning for the Internet of Things.

svm-classification support-vector-regression semi-supervised-learning self-supervised-learning kernel-pca label-propagation


Self-supervised topic modeling

SSL topic model
self-supervised neural topic model
contrastive topic modeling
self-supervised LM-based topic modeling

Various (Miao et al. 2016 for neural topic models; self-supervised objectives widely adopted 2020–2023)

Self-supervised topic modeling combines the interpretable topic discovery of classical topic models with self-supervised learning objectives — such as contrastive loss, masked language modeling, or reconstruction — to learn coherent, semantically rich topics from unlabeled text without human-annotated labels. It bridges classical probabilistic topic models and modern representation learning, yielding topics better aligned with contextual meaning.

Unlabeled or minimally labeled text corpora

Wu, X., Li, C., Zhu, Y., & Miao, Y. (2023). Effective Neural Topic Modeling with Embedding Clustering Regularization. Proceedings of the 40th International Conference on Machine Learning (ICML 2023), PMLR 202, 37335–37357.

lda-topic-model nmf-topic-model sentence-embeddings bert-based-classification semi-supervised-topic-modeling contrastive-learning


Self-supervised Transfer learning

Self-supervised Pre-training for Transfer Learning

representation transfer from self-supervised models

LeCun, Y. (concept); Devlin et al. (BERT, NLP); Chen et al. (SimCLR, vision)

Self-supervised transfer learning combines two powerful paradigms: a model first learns rich representations from unlabeled data using self-supervised pretext tasks, then those learned representations are transferred and fine-tuned on a downstream task with limited labeled data. This approach underlies landmark systems such as BERT in NLP and SimCLR and DINO in computer vision, dramatically reducing labeled-data requirements across many domains.

Learning paradigm (self-supervised pre-training + fine-tuning)

Unlabeled data for pre-training; small labeled dataset for fine-tuning

Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119, 1597–1607.

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT 2019, 4171–4186. Association for Computational Linguistics.

self-supervised-learning transfer-learning semi-supervised-learning few-shot-learning metric-learning self-supervised-few-shot-learning


Self-supervised Transformer

Self-supervised Transformer (Pretraining with Self-generated Supervision)

SSL Transformer
self-supervised pretraining
masked self-attention pretraining
contrastive transformer

Vaswani et al. (architecture); Devlin et al. (BERT self-supervised paradigm)

A self-supervised Transformer is a Transformer network pretrained using automatically constructed supervision signals — such as masked token prediction or next-sentence prediction — rather than human-annotated labels. The resulting representations are then fine-tuned or probed on downstream tasks. BERT, GPT, and ViT (Vision Transformer in masked-image modeling mode) are the most widely known instantiations of this paradigm.

Text, images, audio, or other sequential/structured data

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 30.


Self-supervised Variational Autoencoder

unsupervised VAE with self-supervised pretext tasks

2014 (VAE); self-supervised variant ~2019–2021

Kingma, D. P. & Welling, M. (VAE); self-supervised extensions by various authors from ~2019 onward

A Self-supervised Variational Autoencoder (SS-VAE) combines the generative latent-space learning of a standard VAE with self-supervised pretext tasks — such as contrastive augmentation, masked reconstruction, or rotation prediction — to learn richer, more disentangled representations from unlabeled data without any manual annotation.

Generative model with self-supervised representation learning

Unlabeled or minimally labeled continuous or structured data (images, time series, tabular)

Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014).

Liu, X., Zhang, F., Hou, Z., Mian, L., Wang, Z., Zhang, J., & Tang, J. (2021). Self-Supervised Learning: Generative or Contrastive. IEEE Transactions on Knowledge and Data Engineering, 35(1), 857–876.


Self-supervised Vision Transformer

vision transformer self-supervised pre-training

Self-supervised Vision Transformer (SSL-ViT) applies self-supervised pre-training objectives — such as masked patch prediction (MAE) or self-distillation with no labels (DINO) — to the Vision Transformer architecture, enabling powerful visual representations to be learned from large unlabeled image corpora before any task-specific fine-tuning.

Self-supervised pre-training for vision transformers

He, K., Chen, X., Xie, S., Li, Y., Dollar, P., & Girshick, R. (2022). Masked Autoencoders Are Scalable Vision Learners. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 16000–16009.


Self-supervised Word2Vec

Self-supervised Word2Vec (Skip-gram and CBOW with Self-supervised Objectives)

Word2Vec
word embeddings
Skip-gram model
CBOW model

Word2Vec is a shallow neural network model introduced by Mikolov et al. (2013) that learns dense vector representations of words from large unlabeled text corpora using self-supervised objectives. By training a model to predict surrounding context words (Skip-gram) or a target word from its context (CBOW), it captures rich semantic and syntactic regularities in continuous vector space without any manual annotation.

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. In Proceedings of the International Conference on Learning Representations (ICLR 2013).

Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems (NeurIPS 2013), 26.

glove-embeddings fasttext bert transformer recurrent-neural-network contrastive-learning


SEM

Yapısal Eşitlik Modellemesi (SEM)
structural equation modelling
covariance structure analysis
latent variable modeling

Structural equation modeling is a multivariate statistical framework that simultaneously estimates a measurement model — relating observed indicators to latent constructs — and a structural model specifying directional or reciprocal relationships among those constructs. Rooted in the LISREL tradition developed by Karl Jöreskog in the 1970s, SEM is the standard tool for testing complex theoretical models in the social, behavioural, and management sciences.

Structural path coefficients + fit indices

Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning.

Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling (4th ed.). The Guilford Press.

Byrne, B. M. (2012). Structural Equation Modeling with Mplus: Basic Concepts, Applications, and Programming. Routledge.

confirmatory-factor-analysis exploratory-factor-analysis path-analysis mediation-analysis multilevel-modeling


Semantic Differential

Charles Osgood, George Suci, and Percy Tannenbaum

The Semantic Differential is an attitude measurement technique that assesses the connotative meaning (emotional and evaluative associations) of concepts through ratings on multiple bipolar adjective scales. Developed by Osgood, Suci, and Tannenbaum in the 1950s, the method reveals the affective structure underlying how people perceive concepts—revealing not just what they think, but how they feel about people, brands, ideas, or policies.

Osgood, C. E., Suci, G. J., & Tannenbaum, P. H. (1957). The measurement of meaning. University of Illinois Press.

Snider, J. G., & Osgood, C. E. (1966). Semantic differential technique: A sourcebook. Aldine Publishing Company.

Heise, D. R. (1969). Some methodological issues in semantic differential research. Psychological Bulletin, 72(6), 406-422.

likert-scale attitude-measurement connotative-meaning factor-analysis


Semantic Feature Analysis

Componential (Feature-Based) Semantic Analysis

Semantic Feature Analysis, or Componential Analysis, is a method for understanding word meaning by decomposing concepts into minimal meaningful units called semantic features or components. Developed by Ward Goodenough in 1956, this approach represents the meaning of words as bundles of features (e.g., 'woman' = [human] [adult] [female]), enabling systematic analysis of semantic relationships, kinship systems, plant classifications, and lexical fields. The method is grounded in structural linguistics and has applications in anthropology, cognitive linguistics, and lexicography.

Goodenough, W. H. (1956). Componential analysis and the study of meaning. Language, 32(2), 195-216.

Nida, E. A. (1975). Componential Analysis of Meaning: An Introduction to Semantic Structures. The Hague: Mouton.

Cruse, D. A. (2000). Meaning in Language: An Introduction to Semantics and Pragmatics (2nd ed.). Oxford: Oxford University Press.


Semantic Parsing

Semantic Parsing (Natural Language to Formal Representation)

Anlamsal Ayrıştırma (Semantic Parsing)
NL-to-SQL
text-to-SQL
natural language understanding

Zelle & Mooney (1996) — foundational supervised approach

Semantic parsing is a natural-language-processing task that converts free-text utterances into executable formal representations such as SQL queries, logical forms, or Abstract Meaning Representations (AMR). Established in its supervised learning form by Zelle and Mooney in 1996 and scaled to cross-domain settings by the Spider benchmark (Yu et al., 2018), it bridges the gap between human language and machine-executable structures.

SQL, logical form, AMR (Abstract Meaning Representation)

Executable formal structure (query, logic, graph)

Zelle, J.M. & Mooney, R.J. (1996). Learning to Parse Database Queries Using Inductive Logic Programming. AAAI.

Yu, T. et al. (2018). Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing. EMNLP.

text-classification named-entity-recognition dependency-parsing sentiment-analysis information-extraction


Semantic Role Labeling

SRL
shallow semantic parsing
Anlamsal Rol Etiketleme (SRL)

Semantic role labeling, introduced by Gildea and Jurafsky in 2002, is a natural-language-processing task that assigns semantic roles — who did what to whom, where, when, and how — to the components around a verb (predicate) in a sentence. It turns plain text into structured predicate-argument representations and is a foundational tool for event extraction.

Output: Predicate-argument structure with semantic role labels (who, what, where, when, how)

Gildea, D. & Jurafsky, D. (2002). Automatic Labeling of Semantic Roles. Computational Linguistics, 28(3), 245-288.

Shi, P. & Lin, J. (2019). Simple BERT Models for Relation Extraction and Semantic Role Labeling. arXiv:1904.05255.


Semantic Segmentation

Semantic Segmentation (Dense Pixel-wise Classification)

pixel-wise classification
scene parsing
dense labeling
semantic scene segmentation

Semantic segmentation assigns a class label to every pixel in an image, producing a dense, category-annotated map of the scene. Unlike object detection, which draws bounding boxes, it delineates the exact spatial extent of each class, making it indispensable in medical imaging, autonomous driving, satellite analysis, and any task where precise region boundaries matter.

Dense prediction / pixel-wise classification

Images (RGB, multispectral, or volumetric)

Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440.

Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834–848.


Semantic Similarity

semantic textual similarity
text similarity
Anlamsal Benzerlik Analizi

Nils Reimers & Iryna Gurevych (Sentence-BERT)

Semantic similarity analysis measures how close in meaning two texts are, rather than how many words they share on the surface. Building on the Sentence-BERT work of Reimers and Gurevych (2019), it represents each text as a vector and compares those vectors so that paraphrases score high even when their wording differs.

Embedding-based (cosine similarity over sentence vectors)

Output: Similarity score between a pair of texts

Reimers, N. & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. EMNLP.

Agirre, E. et al. (2013). *SEM 2013 shared task: Semantic Textual Similarity. ACL (*SEM).

bert-embeddings tf-idf document-clustering sentiment-analysis


Semen Quality Evaluation

Semen Quality Evaluation and Breeding Soundness Assessment

breeding soundness examination
seminal analysis
sperm quality assessment

Veterinary Andrologists and Reproductive Physiologists

Semen quality evaluation is a systematic assessment of male animal reproductive capacity, measuring sperm characteristics and overall breeding soundness. Developed by veterinary andrologists in the 1970s, the practice combines objective measures—sperm concentration, motility, morphology—with functional tests to predict fertility potential. Evaluation is essential for identifying suitable breeding animals, managing reproductive health, and ensuring successful artificial insemination (AI) programs.

Coulter, G. H., & Foote, R. H. (1997). Infertility in bulls: Summary of causes and breeding soundness evaluation. Journal of Dairy Science, 62(11), 1812-1829.

Saacke, R. G., White, J. M., & Bame, J. H. (1983). Semen quality and its relationship to mare fertility. Proceedings of the American Association of Equine Practitioners, 29, 391-410.

Thundathil, J., Palma, G. A., & Mapletoft, R. J. (2001). Evaluation of semen quality. Animal Reproduction Science, 64(1-2), 13-32.


Semi-Structured Interview

guided interview
semi-standardized interview
focused interview
SSI

1946 (Merton & Kendall); codified as a standard method through the 1980s–1990s

Robert K. Merton and Patricia Kendall (focused interview, 1946); further systematised by Steinar Kvale

The semi-structured interview is a qualitative data-collection method in which the researcher prepares a set of key questions or topic areas in advance but remains free to probe, follow up, and reorder as the conversation evolves. Unlike structured interviews — which fix every question and sequence — or unstructured interviews — which are entirely open — the semi-structured format balances comparability across participants with the flexibility needed to capture the depth and nuance of individual perspectives. It is the most widely used interview format in social science, health, and education research.

Spoken interview transcripts (text data)

10–30 participants (until thematic saturation)

Kvale, S., & Brinkmann, S. (2009). InterViews: Learning the Craft of Qualitative Research Interviewing (2nd ed.). Sage.

Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage.

phenomenology grounded-theory ethnography case-study thematic-analysis focus-group


Semi-supervised Active Learning

semi-supervised learning with active queries

Muslea, I., Minton, S., & Knoblock, C. A.

Semi-supervised Active Learning (SSAL) is a hybrid learning paradigm that combines active learning's selective query strategy with semi-supervised learning's ability to exploit unlabeled data. The model iteratively selects the most informative unlabeled instances for expert annotation while simultaneously leveraging the large pool of unannotated samples to improve its own representations, dramatically reducing labeling costs while maintaining strong predictive accuracy.

Partially labeled tabular, text, or image data

Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool.

Zhu, X. (2005). Semi-supervised learning literature survey. Technical Report 1530, Computer Sciences, University of Wisconsin-Madison.

semi-supervised-learning active-learning self-training label-propagation gaussian-process-classification query-by-committee


Semi-supervised Apriori Algorithm

Semi-supervised Apriori Algorithm for Constrained Association Rule Mining

constrained Apriori
semi-supervised ARM
knowledge-guided Apriori
labeled-constraint Apriori

Extended from Agrawal & Srikant (1994); constrained variants developed by Liu, Hsu & Ma (1999) and others

The Semi-supervised Apriori algorithm extends the classic Apriori frequent-itemset miner by injecting background knowledge or labeled constraints — such as must-link pairs, forbidden items, or user-specified minimum support thresholds per group — to bias discovery toward practically meaningful association rules and reduce the search space.

Constrained association rule mining algorithm

Transactional / tabular categorical data with partial supervision or domain constraints

Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 487–499.

Liu, B., Hsu, W., & Ma, Y. (1999). Mining association rules with multiple minimum supports. Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 337–341.

apriori fp-growth semi-supervised-learning association-rule-mining constrained-clustering collaborative-filtering


Semi-supervised Association Rules

semi-supervised ARM
label-guided association rule mining
constrained association rule mining
semi-supervised pattern discovery

Liu, B.; Hsu, W.; Ma, Y. (and subsequent researchers)

Semi-supervised association rule mining extends classical association rule learning by incorporating a small amount of labeled data alongside a larger unlabeled dataset. It uses known class information or user-provided constraints to guide the discovery of rules that are both statistically frequent and semantically meaningful, bridging unsupervised pattern mining with light supervision.

Transactional / tabular data with partial labels

Liu, B., Hsu, W., & Ma, Y. (2003). Integrating Classification and Association Rule Mining. In Proceedings of the 4th IEEE International Conference on Data Mining (ICDM), pp. 339–346.

apriori-algorithm fp-growth semi-supervised-learning classification-association-rules label-propagation constrained-pattern-mining


Semi-supervised Autoencoder-based Anomaly Detection

partially labeled autoencoder anomaly detection

Semi-supervised Autoencoder Anomaly Detection trains a neural autoencoder primarily on normal (unlabeled) data, then uses a small set of labeled anomalies to refine decision boundaries, detecting anomalies as samples with high reconstruction error. It bridges the gap between purely unsupervised autoencoders and fully supervised classifiers when labels are scarce but some known anomalies exist.

Continuous, high-dimensional, or sequential tabular/image/time-series data

Ruff, L., Vandermeulen, R. A., Franks, B. J., Müller, K.-R., & Kloft, M. (2020). Deep Semi-Supervised Anomaly Detection. In International Conference on Learning Representations (ICLR 2020).

Zong, B., Song, Q., Min, M. R., Cheng, W., Lumezanu, C., Cho, D., & Chen, H. (2018). Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection. In International Conference on Learning Representations (ICLR 2018).

autoencoder-anomaly-detection one-class-svm semi-supervised-learning gaussian-mixture-model isolation-forest semi-supervised-one-class-svm


Semi-supervised Bagging

Semi-supervised Bagging (Bootstrap Aggregating with Unlabeled Data)

SS-Bagging
semi-supervised bootstrap aggregating
self-training bagging
bagging with pseudo-labels

Various (Breiman bagging + semi-supervised extensions, 1990s–2000s)

Semi-supervised Bagging extends the classical bagging ensemble to settings where labeled training examples are scarce but large amounts of unlabeled data are available. Base learners trained on labeled data assign pseudo-labels to unlabeled examples; the expanded dataset is then used to grow a diverse ensemble whose aggregated vote is more accurate and more stable than any single model trained on the limited labeled set alone.

Semi-supervised ensemble (bagging variant)

Labeled and unlabeled tabular or feature-vector data

Bennett, K. P., & Demiriz, A. (1999). Semi-supervised support vector machines. Advances in Neural Information Processing Systems, 11. MIT Press.

Li, M., & Zhou, Z.-H. (2005). SETRED: Self-training with editing. In Proceedings of the 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), LNAI 3518, pp. 611–621. Springer.

random-forest self-training label-propagation co-training gradient-boosting semi-supervised-learning


Semi-supervised BERT-based Text Classification

Semi-supervised BERT
BERT SSL Classification
BERT with Unlabeled Data
BERT Semi-supervised Fine-tuning

Multiple groups (Xie et al.; Chen et al.; Devlin et al. for BERT base)

Semi-supervised BERT-based classification fine-tunes a pre-trained BERT encoder on a small pool of labeled text examples while simultaneously leveraging a much larger body of unlabeled text — via consistency training, pseudo-labeling, or data augmentation — to produce high-quality classifiers even when manual annotation is scarce.

Semi-supervised fine-tuning of pre-trained transformer

Text with few labeled and many unlabeled examples

Xie, Q., Dai, Z., Hovy, E., Luong, T., & Le, Q. (2020). Unsupervised Data Augmentation for Consistency Training. Advances in Neural Information Processing Systems (NeurIPS), 33, 27780–27792.

Chen, J., Yang, Z., & Yang, D. (2020). MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), 2147–2157.


Semi-supervised Boosting

Semi-supervised Boosting (Boosting with Unlabeled Data)

SemiBoost
SSL boosting
boosting with unlabeled data
semi-supervised ensemble boosting

Mallapragada, P. K.; Bennett, K. P.; and others

Semi-supervised Boosting is an ensemble learning paradigm that extends classical boosting algorithms — such as AdaBoost — to exploit both labeled and unlabeled data. By propagating label information through a similarity structure over unlabeled instances, it trains stronger classifiers than supervised boosting alone when labeled data are scarce.

Mallapragada, P. K., Jin, R., Jain, A. K., & Liu, Y. (2009). SemiBoost: Boosting for Semi-supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(11), 2000–2014.

Bennett, K. P., & Demiriz, A. (1999). Semi-supervised Support Vector Machines. Advances in Neural Information Processing Systems (NIPS), 11, 368–374.

adaboost gradient-boosting xgboost label-propagation self-training semi-supervised-learning


Semi-supervised CatBoost

Semi-supervised CatBoost (Gradient Boosting with Partially Labeled Data)

semi-supervised gradient boosting with CatBoost

2018 (CatBoost); semi-supervised learning framework predates 2006

Prokhorenkova et al. (CatBoost); semi-supervised paradigm from Chapelle et al.

Semi-supervised CatBoost applies CatBoost's ordered gradient boosting framework to settings where only a fraction of training instances carry labels, leveraging unlabeled data through pseudo-labeling or consistency-based strategies to improve model accuracy beyond what labeled data alone would allow.

Semi-supervised ensemble (gradient boosting)

Tabular data, partially labeled (mixed labeled and unlabeled samples)

Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: unbiased boosting with categorical features. In Advances in Neural Information Processing Systems (NeurIPS), 31.

Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press.

semi-supervised-xgboost semi-supervised-gradient-boosting semi-supervised-random-forest catboost gradient-boosting self-supervised-catboost


Semi-supervised Convolutional Neural Network (SSL-CNN)

SSL-CNN
semi-supervised CNN
self-training CNN
pseudo-label CNN

Lee, D.-H.; Tarvainen, A. & Valpola, H. (among others)

A Semi-supervised CNN trains a convolutional network on a small labeled image set and a larger pool of unlabeled images simultaneously, using techniques such as pseudo-labeling and consistency regularization to extract supervisory signal from unlabeled data. This strategy closes much of the performance gap caused by scarce annotations without requiring additional human labeling effort.

Lee, D.-H. (2013). Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. ICML Workshop on Challenges in Representation Learning.

Tarvainen, A. & Valpola, H. (2017). Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in Neural Information Processing Systems (NeurIPS), 30.


Semi-supervised DBSCAN

Semi-supervised Density-Based Spatial Clustering of Applications with Noise

DBSCAN with must-link/cannot-link constraints

Ester, M. et al. (DBSCAN base); semi-supervised extensions by multiple authors (2000s–2010s)

Semi-supervised DBSCAN extends the canonical density-based clustering algorithm (Ester et al., 1996) by incorporating a small set of pairwise or label constraints — must-link pairs that must share a cluster, cannot-link pairs that must be separated, or a handful of known labels — to guide cluster formation while retaining DBSCAN's ability to discover arbitrary-shaped clusters and flag noise points.

Unlabeled data with a small set of pairwise or label constraints

Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), pp. 226–231. AAAI Press.

Zhu, X., & Goldberg, A. B. (2009). Introduction to Semi-Supervised Learning. Morgan & Claypool Publishers.

dbscan hdbscan semi-supervised-k-means semi-supervised-gaussian-mixture-model k-means constrained-clustering


Semi-supervised Decision Tree

SSDT
semi-supervised tree induction
self-training decision tree
label-propagation tree

Various (Levin & Shapiro; Zhu & Goldberg lineage)

A Semi-supervised Decision Tree extends standard decision tree induction — such as CART or C4.5 — to exploit unlabeled observations alongside the labeled training set. By iteratively assigning tentative labels to unlabeled data and incorporating them into the growing or splitting process, the algorithm can achieve better accuracy than a fully supervised tree trained on the labeled subset alone, which is especially valuable when labeling is expensive or time-consuming.

Tabular data with mixed labeled and unlabeled observations

Levin, E. & Shapiro, E. (2000). Learning Decision Trees from Semi-labeled Examples. Proceedings of the ICML Workshop on Attribute-Value and Relational Learning.

Zhu, X. & Goldberg, A. B. (2009). Introduction to Semi-Supervised Learning. Morgan & Claypool Publishers.

decision-tree random-forest self-training label-propagation co-training gradient-boosting


Semi-supervised Diffusion Model

Semi-supervised Diffusion Model for Generative Learning with Partial Labels

Semi-supervised score-based generative model

Multiple groups (Ho et al., Song et al., and successors)

A semi-supervised diffusion model extends the denoising diffusion probabilistic framework to settings where only a fraction of training samples carry class labels. By combining an unconditional diffusion backbone with a lightweight classifier trained on labeled examples, it learns to generate high-quality, label-conditioned outputs while still exploiting the structure in unlabeled data.

Generative model with semi-supervised guidance

Images, text, audio, or structured data with partial class labels

Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., & Ganguli, S. (2015). Deep Unsupervised Learning using Nonequilibrium Thermodynamics. Proceedings of the 32nd International Conference on Machine Learning (ICML), 2256–2265.

Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851.

denoising-diffusion-probabilistic-model score-based-generative-model variational-autoencoder semi-supervised-learning conditional-diffusion-model generative-adversarial-network


Semi-supervised Doc2Vec

Semi-supervised Paragraph Vector (Semi-supervised Doc2Vec)

Semi-supervised Paragraph Vector
SS-Doc2Vec
Label-guided PV-DBOW
Semi-supervised PV-DM

Le, Q. V. & Mikolov, T. (base Doc2Vec); semi-supervised extensions by various authors circa 2015–2019

Semi-supervised Doc2Vec extends the Paragraph Vector framework of Le and Mikolov (2014) by training dense document embeddings on both labeled and unlabeled corpora simultaneously, using available class labels as an auxiliary signal to steer the representation toward task-relevant structure while still exploiting the full unlabeled collection for generalization.

Le, Q. V., & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. Proceedings of the 31st International Conference on Machine Learning (ICML 2014), PMLR 32(2), 1188–1196.

doc2vec word2vec bert-fine-tuning semi-supervised-text-classification label-propagation sentence-transformers


Semi-supervised Federated learning

SSL-FL
federated semi-supervised learning
FSSL
semi-supervised distributed learning

Jeong, W. et al. / multiple independent groups

Semi-supervised federated learning (SSFL) trains a shared model across many decentralized clients — each holding private data — when only a subset of clients or a subset of local samples carry labels. It combines the privacy-preserving coordination of federated learning with the label-efficiency of semi-supervised techniques such as pseudo-labeling and consistency regularization, enabling strong model quality without centralizing sensitive data.

Distributed semi-supervised learning framework

Partially labeled distributed tabular, image, or text data

Jeong, W., Yoon, J., Yang, E., & Hwang, S. J. (2020). Federated Semi-Supervised Learning with Inter-Client Consistency. International Conference on Learning Representations (ICLR 2021).

Zhang, Z., Chen, Y., Yu, H., & Lu, J. (2021). SemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labeling. arXiv preprint arXiv:2108.09412.

federated-learning semi-supervised-learning self-supervised-learning transfer-learning few-shot-learning online-federated-learning


Semi-supervised Few-shot Learning

SS-FSL
semi-supervised meta-learning
few-shot learning with unlabeled data
low-label few-shot learning

Ren, M. et al. (ICLR 2018); builds on Finn et al. (MAML, 2017)

Semi-supervised Few-shot Learning (SS-FSL) trains models to classify new classes from only a handful of labeled examples per class, while simultaneously leveraging a pool of unlabeled data to enrich class representations. By combining meta-learning episodes with soft pseudo-label assignment for unlabeled samples, it achieves notably higher accuracy than purely supervised few-shot methods when abundant unlabeled data is available.

Meta-learning with unlabeled auxiliary data

Labeled support set (few examples per class) + unlabeled distractor/auxiliary examples

Ren, M., Triantafillou, E., Ravi, S., Snell, J., Swersky, K., Tenenbaum, J. B., Larochelle, H., & Zemel, R. S. (2018). Meta-learning for semi-supervised few-shot classification. In International Conference on Learning Representations (ICLR 2018).

Finn, C., Abbeel, P., & Levine, S. (2017). Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of the 34th International Conference on Machine Learning (ICML 2017), PMLR 70, 1126–1135.

few-shot-learning semi-supervised-learning meta-learning prototypical-networks self-supervised-learning transfer-learning


Semi-supervised FP-growth

SS-FP-growth
constrained FP-growth
label-guided frequent pattern mining
semi-supervised frequent itemset mining

Extensions of Han, Pei & Yin (2000); semi-supervised variants developed by various authors in the 2000s–2010s

Semi-supervised FP-growth extends the classical Frequent Pattern growth algorithm by incorporating partial labels, user-defined constraints, or class-level information to guide frequent itemset discovery. Instead of mining all patterns indiscriminately, it focuses on patterns that are both statistically frequent and semantically meaningful given the available supervision signal.

Transactional / categorical data with partial labels or constraints

Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 1–12.

fp-growth apriori constrained-association-rule-mining semi-supervised-clustering decision-tree random-forest


Semi-supervised GAN

Semi-supervised Generative Adversarial Network

semi-supervised generative adversarial network

Semi-supervised GAN (SGAN) extends the standard GAN discriminator to simultaneously classify labeled examples into K real classes and detect generated fakes as a (K+1)-th class, letting the generator's synthetic data act as implicit regularization and allowing strong classifiers to be trained with very few labeled examples.

Images, tabular, or text with small labeled and large unlabeled sets

Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016). Improved Techniques for Training GANs. Advances in Neural Information Processing Systems (NeurIPS), 29.

Odena, A. (2016). Semi-Supervised Learning with Generative Adversarial Networks. ICML Workshop on Generative Adversarial Networks.


Semi-supervised Gaussian Mixture Model

partially labeled Gaussian mixture model

Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T.

The Semi-supervised Gaussian Mixture Model (SS-GMM) is a generative probabilistic classifier that fits a Gaussian mixture to both labeled and unlabeled data using the Expectation-Maximization algorithm. Labeled points constrain component assignments while unlabeled points improve density estimates, enabling effective learning when annotations are scarce.

Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press.

Nigam, K., McCallum, A. K., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39(2-3), 103-134.

gaussian-mixture-model expectation-maximization semi-supervised-learning naive-bayes-classifier label-propagation variational-autoencoder


Semi-supervised Gaussian Process

Semi-supervised Gaussian Process Regression and Classification

SS-GP
semi-supervised GP
Gaussian process with unlabeled data
GP manifold learning

Semi-supervised Gaussian Process extends the probabilistic GP framework to exploit unlabeled data alongside a small set of labeled observations. By placing a GP prior over functions and leveraging the geometric structure revealed by unlabeled inputs, it learns more accurate and better-calibrated predictors than a purely supervised GP when labels are scarce, making it well suited for scientific and medical problems where annotation is expensive.

Continuous, binary, or multi-class; partially labeled tabular or structured data

Lawrence, N. D., & Jordan, M. I. (2004). Semi-supervised learning via Gaussian processes. In Advances in Neural Information Processing Systems (NIPS), 17, 753–760. MIT Press.

Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press.

gaussian-process semi-supervised-learning gaussian-mixture-model semi-supervised-support-vector-machine semi-supervised-random-forest bayesian-gaussian-process


Semi-supervised Gradient Boosting

Semi-supervised Gradient Boosting (Self-training / Pseudo-labeling with Gradient Boosted Trees)

pseudo-label gradient boosting
self-training GBM
semi-supervised GBT
label-propagation boosting

Chapelle, Scholkopf & Zien (eds.); applied to GBM variants in subsequent literature

Semi-supervised gradient boosting combines gradient boosted trees with self-training or pseudo-labeling to exploit large pools of unlabeled data alongside a small labeled set. An initial GBM fit on labeled data assigns confident predictions to unlabeled examples; those pseudo-labeled points are folded back into training and the model is re-boosted, iterating until convergence. This allows practitioners to harness cheap unlabeled data when labels are scarce or expensive.

Semi-supervised ensemble (self-training + gradient boosted trees)

Yarowsky, D. (1995). Unsupervised word sense disambiguation rivaling supervised methods. Proceedings of ACL 1995, 189–196. (Foundational self-training framework underlying pseudo-label approaches.)

Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press.

semi-supervised-learning gradient-boosting xgboost self-supervised-learning semi-supervised-random-forest boosting


Semi-supervised Graph Neural Network

Semi-supervised Graph Neural Network (GNN with Label Propagation)

2017 (GCN formulation); 2004 (label propagation roots)

Kipf, T. N. & Welling, M. (canonical formulation); Zhou et al. (label propagation precursor)

A semi-supervised graph neural network trains a GNN on a graph where only a small fraction of nodes carry labels, using neighborhood message-passing to spread information from labeled nodes to unlabeled ones. The approach, popularised by Kipf and Welling's 2017 Graph Convolutional Network, achieves strong node-classification accuracy even when labeled examples are scarce.

Semi-supervised graph representation learning

Graph-structured data with node features and a small fraction of labeled nodes

Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR 2017).

Zhou, D., Bousquet, O., Lal, T. N., Weston, J., & Scholkopf, B. (2004). Learning with Local and Global Consistency. Advances in Neural Information Processing Systems (NeurIPS 2004), 17.

graph-neural-network graph-convolutional-network semi-supervised-learning label-propagation self-supervised-graph-neural-network node-classification


Semi-supervised GRU

Semi-supervised GRU
SSL-GRU
GRU with unlabeled data
semi-supervised recurrent classifier

Dai, A. M. & Le, Q. V. (semi-supervised sequence learning); Cho, K. et al. (GRU architecture)

Semi-supervised GRU applies the Gated Recurrent Unit architecture to settings where only a small fraction of sequential data is labeled. By first pre-training or jointly training on abundant unlabeled sequences — through language modeling, auto-encoding, or consistency regularization — and then fine-tuning on labeled examples, the model exploits the full corpus to learn richer sequence representations than supervised-only training would allow.

Sequential / time-series / text data (small labeled + large unlabeled)

Dai, A. M., & Le, Q. V. (2015). Semi-supervised Sequence Learning. Advances in Neural Information Processing Systems (NeurIPS), 28.

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. EMNLP 2014.


Semi-supervised HDBSCAN

Semi-supervised Hierarchical Density-Based Spatial Clustering of Applications with Noise

Semi-supervised hierarchical density clustering

McInnes, L.; Healy, J. (base HDBSCAN); semi-supervised extensions by various authors

Semi-supervised HDBSCAN extends the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm by incorporating partial supervision — such as must-link and cannot-link pairwise constraints or a small set of labeled examples — to guide the density-based cluster hierarchy toward cluster assignments that are consistent with available domain knowledge.

Continuous or mixed tabular data; optional partial cluster labels or pairwise constraints

McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.

hdbscan semi-supervised-dbscan semi-supervised-k-means semi-supervised-gaussian-mixture-model dbscan k-means


Semi-supervised Image Classification

Semi-supervised Image Classification with Deep Neural Networks

SSL image classification
semi-supervised CNN classification
pseudo-label image classification
label-efficient image classification

Lee, D.-H. (pseudo-label); Sohn et al. (FixMatch)

Semi-supervised image classification trains deep neural networks on a small set of labeled images together with a much larger pool of unlabeled images. Techniques such as pseudo-labeling, consistency regularization, and confidence thresholding allow the model to leverage the structure of unlabeled data, dramatically reducing the need for expensive manual annotation while approaching fully-supervised accuracy.

Lee, D.-H. (2013). Pseudo-Label: The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks. ICML 2013 Workshop on Challenges in Representation Learning.

Sohn, K., Berthelot, D., Li, C.-L., Zhang, Z., Carlini, N., Cubuk, E. D., Kurakin, A., Zhang, H., & Raffel, C. (2020). FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence. Advances in Neural Information Processing Systems, 33, 596–608.


Semi-supervised Instance Segmentation

Semi-supervised Mask R-CNN
pseudo-label instance segmentation
label-efficient instance segmentation
SSIS

Multiple independent research groups (2018–2021)

Semi-supervised instance segmentation trains a model to detect and delineate every object instance in an image using a small labeled set and a large unlabeled image corpus. By generating pseudo-labels from confident predictions on unlabeled images and enforcing consistency under augmentation, the approach achieves competitive mask accuracy at a fraction of the full annotation cost.

Semi-supervised deep learning for dense prediction

Images with a small labeled set and a large unlabeled set

Hu, H., Wei, P., Zheng, H., Bai, X., Wei, Y., & Chen, Y. (2021). Semi-supervised Semantic Segmentation via Adaptive Equalization Learning. Advances in Neural Information Processing Systems (NeurIPS), 34, 22106–22118.

Xu, M., Zhang, Z., Wei, F., Hu, H., Bai, X., & Jiang, Y.-G. (2021). End-to-End Semi-Supervised Object Detection with Soft Teacher. IEEE/CVF International Conference on Computer Vision (ICCV), 3060–3069.


Semi-supervised Isolation Forest

Semi-supervised Isolation Forest for Anomaly Detection

SSIF
semi-supervised iForest
label-guided Isolation Forest
partially supervised Isolation Forest

Extended from Liu, F.T., Ting, K.M., and Zhou, Z-H. (iForest, 2008); semi-supervised variants developed by multiple authors ca. 2013–2020

Semi-supervised Isolation Forest extends the classic Isolation Forest anomaly detector by incorporating a small set of labeled anomaly (and possibly normal) examples alongside a large unlabeled dataset. This label guidance adjusts the model's anomaly scores so that known anomalies are separated more reliably, bridging the gap between fully unsupervised and fully supervised detection.

Ensemble anomaly detection (semi-supervised extension)

Tabular, mixed; small set of labeled anomalies + large unlabeled pool

Görnitz, N., Kloft, M., Rieck, K., & Brefeld, U. (2013). Toward supervised anomaly detection. Journal of Artificial Intelligence Research, 46, 235–262.

isolation-forest one-class-svm local-outlier-factor random-forest autoencoder-anomaly-detection semi-supervised-learning


Semi-supervised K-means

constrained K-means
seeded K-means
partially supervised K-means
SS-K-means

Wagstaff, K. et al. (constrained); Basu, S. et al. (seeded)

Semi-supervised K-means extends standard K-means clustering by incorporating partial supervision — either a small set of labeled seed points or pairwise must-link and cannot-link constraints — to guide cluster formation. It bridges unsupervised clustering and fully supervised classification, enabling more meaningful clusters when labels are scarce but costly to obtain in full.

Numerical (continuous); partial cluster labels or pairwise constraints

Wagstaff, K., Cardie, C., Rogers, S., & Schroedl, S. (2001). Constrained K-means Clustering with Background Knowledge. In Proceedings of the 18th International Conference on Machine Learning (ICML 2001), pp. 577–584.

Basu, S., Banerjee, A., & Mooney, R. J. (2002). Semi-supervised Clustering by Seeding. In Proceedings of the 19th International Conference on Machine Learning (ICML 2002), pp. 27–34.

k-means semi-supervised-learning gaussian-mixture-model dbscan active-learning spectral-clustering


Semi-supervised K-nearest neighbors

Semi-supervised K-Nearest Neighbors (Label Propagation via KNN Graph)

SS-KNN
semi-supervised KNN
KNN label propagation
graph-based semi-supervised KNN

2002 (semi-supervised extension); 1967 (KNN base)

Zhu, X. & Ghahramani, Z. (label propagation); Cover, T. & Hart, P. (KNN base)

Semi-supervised KNN extends the classic K-nearest neighbors algorithm to exploit large pools of unlabeled data alongside a small labeled set. By building a KNN graph over all observations and propagating known labels through the graph's edges, the method infers labels for unlabeled points without requiring expensive manual annotation of every sample.

Semi-supervised classifier / label propagation

Tabular features; requires a small labeled set and a larger unlabeled set

Zhu, X. & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.

Chapelle, O., Scholkopf, B. & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press.

k-nearest-neighbors semi-supervised-learning label-propagation semi-supervised-gaussian-process semi-supervised-support-vector-machine graph-based-semi-supervised-learning


Semi-supervised LDA Topic Model

Semi-supervised Latent Dirichlet Allocation Topic Model

Labeled LDA
Seeded LDA
Constrained LDA
SS-LDA

Semi-supervised LDA extends standard Latent Dirichlet Allocation by incorporating a small amount of supervision — seed words, labeled documents, or must-link/cannot-link word constraints — to guide topic discovery toward semantically coherent, interpretable themes. It bridges unsupervised topic modeling and fully supervised text classification, making it especially valuable when full annotation is costly.

Semi-supervised probabilistic topic model

Text corpora (partially labeled documents or seed words)

Ramage, D., Hall, D., Nallapati, R., & Manning, C. D. (2009). Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. Proceedings of EMNLP, 248–256.

Andrzejewski, D., Zhu, X., & Craven, M. (2009). Incorporating domain knowledge into topic modeling via Dirichlet Forest priors. Proceedings of ICML, 25–32.

lda-topic-model semi-supervised-nmf-topic-model topic-modeling bert-based-classification semi-supervised-transformer sentence-embeddings


Semi-supervised Learning

Semi-supervised Learning (Combined Labeled and Unlabeled Data Training)

SSL
semi-supervised machine learning
transductive learning
label-efficient learning

Vapnik, V. N. and others (community of researchers, 1970s–2000s)

Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.

Tabular, image, text, or sequential data with a mix of labeled and unlabeled instances

Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press.

Zhu, X. (2005). Semi-supervised learning literature survey. Technical Report 1530, University of Wisconsin-Madison.

self-supervised-learning transfer-learning active-learning few-shot-learning semi-supervised-random-forest gaussian-mixture-model


Semi-supervised LightGBM

Semi-supervised Learning with Light Gradient Boosting Machine

SSL-LightGBM
pseudo-label LightGBM
self-training LightGBM
semi-supervised GBDT

Ke, G. et al. (LightGBM); semi-supervised extension via community practice and research

Semi-supervised LightGBM combines LightGBM's highly efficient gradient boosting framework with semi-supervised strategies — most commonly pseudo-labeling or self-training — to exploit large pools of unlabeled data alongside a smaller labeled set, improving predictive performance when obtaining labels is costly or time-consuming.

Semi-supervised gradient boosting ensemble

Tabular (labeled and unlabeled instances)

Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154.

Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press.

semi-supervised-gradient-boosting semi-supervised-xgboost semi-supervised-random-forest self-training lightgbm pseudo-labeling


Semi-supervised Linear Regression

Semi-supervised Linear Regression (Linear Model with Labeled and Unlabeled Data)

SSL linear regression
semi-supervised least squares
transductive linear regression
label-efficient linear regression

Chapelle, O.; Scholkopf, B.; Zien, A. (seminal synthesis); Zhou & Li (co-training formulation)

Semi-supervised linear regression fits a linear model on a small labeled dataset and then leverages a larger pool of unlabeled observations to improve coefficient estimates and generalization. By generating pseudo-labels for unlabeled points and iteratively refining the model, it achieves better predictive accuracy than a purely supervised linear model trained on scarce labels alone.

Mixed labeled and unlabeled continuous/tabular data

Zhou, Z.-H., & Li, M. (2005). Semi-supervised regression with co-training. Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI), 908–913.

linear-regression-ml semi-supervised-learning self-supervised-linear-regression regularized-linear-regression label-propagation expectation-maximization


Semi-supervised Logistic Regression

Semi-supervised Logistic Regression (Self-training and EM-based variants)

SSL logistic regression
semi-supervised LR
EM logistic regression
self-training logistic classifier

Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training)

Semi-supervised logistic regression extends the standard logistic classifier by incorporating unlabeled data during training. Using self-training, expectation-maximization, or label-propagation wrappers, it iteratively assigns soft labels to unlabeled examples and refines model parameters, improving generalization when labeled data are scarce relative to the full dataset.

Labeled and unlabeled tabular or text data

Nigam, K., McCallum, A., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39, 103–134.

Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-supervised Learning. MIT Press.

logistic-regression-ml semi-supervised-learning self-supervised-logistic-regression semi-supervised-naive-bayes label-propagation support-vector-machine


Semi-supervised LSTM

Semi-supervised Long Short-Term Memory Network

SSL-LSTM
semi-supervised sequence model
LSTM with unlabeled data
pseudo-label LSTM

Hochreiter, S. & Schmidhuber, J. (LSTM); semi-supervised extensions by various authors (2015–2020)

Semi-supervised LSTM combines the sequential memory of Long Short-Term Memory networks with semi-supervised learning strategies — using a small labeled dataset alongside a large pool of unlabeled sequences. The model is pretrained or regularized on unlabeled data, then fine-tuned on labeled examples, delivering strong generalization when labeled data is scarce.

Sequential / temporal data (text, time series, sensor streams); small labeled set + large unlabeled set

Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.

Rasmus, A., Berglund, M., Honkala, M., Valpola, H., & Raiko, T. (2015). Semi-supervised learning with ladder networks. Advances in Neural Information Processing Systems, 28.

lstm bidirectional-lstm self-training variational-autoencoder transformer semi-supervised-learning


Semi-supervised Metric Learning

SSML
semi-supervised distance learning
constrained metric learning
weakly supervised metric learning

Yeung, D.-Y. & Chang, H.; Davis, J. V. & Dhillon, I. S.

Semi-supervised metric learning learns a task-adapted distance function by combining a small set of labeled pairwise constraints — must-link and cannot-link pairs — with the geometric structure of a much larger pool of unlabeled data. The result is a Mahalanobis-style or kernel-based distance that reflects both supervision and data topology, improving downstream tasks such as nearest-neighbor classification and clustering.

Hybrid supervised/unsupervised distance learning

Tabular or embedding vectors; partially labeled

Yeung, D.-Y., & Chang, H. (2007). A kernel approach for semi-supervised metric learning. IEEE Transactions on Neural Networks, 18(1), 141–149.

Davis, J. V., & Dhillon, I. S. (2008). Structured metric learning for high dimensional problems. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 195–203.

metric-learning semi-supervised-learning k-nearest-neighbors few-shot-learning self-supervised-learning transfer-learning


Semi-supervised Multilayer Perceptron

Semi-supervised Multilayer Perceptron (SSL-MLP)

partially supervised multilayer perceptron

Chapelle, O.; Scholkopf, B.; Zien, A. (eds.); Lee, D.-H.

A semi-supervised multilayer perceptron (SSL-MLP) is a feedforward neural network trained on a small pool of labeled examples together with a larger pool of unlabeled examples. By combining supervised cross-entropy loss on labeled data with an unsupervised consistency or pseudo-label objective on unlabeled data, it extracts far more signal from the data than a purely supervised MLP trained on labels alone.

Semi-supervised feedforward neural network

Tabular, text, image (mixed labeled and unlabeled)

Lee, D.-H. (2013). Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. ICML 2013 Workshop on Challenges in Representation Learning.


Semi-supervised Naive Bayes

Semi-supervised Naive Bayes (EM-augmented Generative Classifier)

SSL Naive Bayes
EM-Naive Bayes
semi-supervised generative classifier
Nigam et al. text classifier

Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T.

Semi-supervised Naive Bayes extends the classic Naive Bayes generative model to exploit large pools of unlabeled data alongside a small labeled set. Using Expectation-Maximization, it iteratively infers soft class assignments for unlabeled examples and re-estimates class and feature parameters, yielding substantially better classifiers when labeled examples are scarce.

Nigam, K., McCallum, A. K., Thrun, S., & Mitchell, T. (2000). Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning, 39(2–3), 103–134.

naive-bayes semi-supervised-learning expectation-maximization logistic-regression semi-supervised-support-vector-machine gaussian-mixture-model


Semi-supervised NMF Topic Model

Semi-supervised Non-negative Matrix Factorization Topic Model

SS-NMF
guided NMF
constrained NMF topic model
seed-guided NMF

2001 (NMF); semi-supervised variants from ~2010s

Lee & Seung (NMF); semi-supervised extensions by Jagarlamudi et al. and others

Semi-supervised Non-negative Matrix Factorization (NMF) Topic Model extends unsupervised NMF by incorporating user-provided seed words or label constraints to steer discovered topics toward domain-relevant themes. It factorizes a document-term matrix into interpretable non-negative components while respecting lexical priors, yielding coherent, application-aligned topics even from modest corpora.

Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562.

Jagarlamudi, J., Daume, H., & Udupa, R. (2012). Incorporating lexical priors into topic models. Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2012), 204–213.

nmf-topic-model lda-topic-model semi-supervised-lda-topic-model topic-modeling sentence-embeddings semi-supervised-transformer


Semi-supervised Object Detection

Semi-supervised Object Detection (Pseudo-label / Mean-Teacher Paradigm)

SSOD
semi-supervised detection
pseudo-label object detection
label-efficient object detection

Sohn et al. (STAC); Liu et al. (Unbiased Teacher)

Semi-supervised object detection trains a detector on a small labeled image set and a large unlabeled image set. A teacher model generates pseudo-labels for unlabeled images, and a student model learns from both real and pseudo-labeled data, dramatically reducing the expensive manual bounding-box annotation burden while achieving accuracy competitive with fully supervised baselines.

Images with a small labeled subset and a large unlabeled subset

Sohn, K., Zhang, Z., Li, C.-L., Zhang, H., Lee, C.-Y., & Pfister, T. (2020). A Simple Semi-Supervised Learning Framework for Object Detection. arXiv preprint arXiv:2005.04757.

Liu, Y.-C., Ma, C.-Y., He, Z., Kuo, C.-W., Chen, K., Zhang, P., Wu, B., Kira, Z., & Vajda, P. (2021). Unbiased Teacher for Semi-Supervised Object Detection. ICLR 2021.


Semi-supervised One-class SVM

Semi-supervised One-Class Support Vector Machine

SS-OCSVM
semi-supervised OC-SVM
semi-supervised novelty detection SVM
transductive one-class SVM

Extension of Scholkopf et al. (2001); semi-supervised variants studied ca. 2004–2010

Semi-supervised One-class SVM extends the classic One-class SVM anomaly detector by incorporating unlabeled observations alongside a small set of known normal examples. The unlabeled data helps the model learn a tighter, more informative decision boundary in feature space, reducing false positives and improving anomaly recall compared to the purely unsupervised baseline.

Semi-supervised anomaly / novelty detection

Continuous/numeric features; labeled normal examples plus unlabeled pool

Munoz, A. & Muruzabal, J. (2004). Self-Organising Maps for Outlier Detection. Neurocomputing, 58–60, 953–956.

Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471.

one-class-svm semi-supervised-learning support-vector-machine isolation-forest autoencoder-anomaly-detection gaussian-process


Semi-supervised Online Learning

Semi-supervised Online Learning (Incremental Learning with Partially Labeled Streams)

SSOL
online semi-supervised learning
semi-supervised incremental learning
streaming semi-supervised learning

Goldberg, A.; Li, M.; Zhu, X. (among key contributors)

Semi-supervised Online Learning combines the incremental update style of online learning with the ability to exploit unlabeled examples, enabling models to improve continuously from a data stream in which only a small fraction of arriving instances carry ground-truth labels. It is especially valuable when labeling is expensive or delayed but data arrives in real time.

Hybrid learning paradigm (online + semi-supervised)

Sequential, partially labeled data streams

Goldberg, A., Li, M., & Zhu, X. (2008). Online manifold regularization: A new learning setting and empirical study. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2008), Lecture Notes in Computer Science, 5211, 393–407. Springer.

Zhu, X., & Goldberg, A. B. (2009). Introduction to Semi-Supervised Learning. Morgan & Claypool Publishers.

semi-supervised-learning online-learning self-training label-propagation active-learning incremental-learning


Semi-supervised Question Answering

Semi-supervised Question Answering (Self-Training and Consistency-Based NLP)

Semi-supervised QA
Self-training for QA
Pseudo-labeled Question Answering
SSL-QA

Multiple (Chapelle et al.; Zhu; Clark et al. for NLP applications)

Semi-supervised question answering (QA) trains a model on a small labeled set of question-answer pairs, then generates pseudo-labels on a large unlabeled corpus and retrains iteratively. This self-training loop dramatically increases effective training data without the cost of full manual annotation, achieving strong performance on reading comprehension, open-domain QA, and machine reading tasks.

Semi-supervised learning applied to extractive/generative QA

Text (question-context-answer triples; labeled + unlabeled corpora)

Clark, K., Luong, M.-T., Le, Q. V., & Manning, C. D. (2020). ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. In Proceedings of ICLR 2020.

Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. V. (2019). XLNet: Generalized Autoregressive Pretraining for Language Understanding. In Advances in Neural Information Processing Systems (NeurIPS 2019).


Semi-supervised Random Forest

SSL-RF
semi-supervised forest
label-propagation random forest
self-training random forest

Semi-supervised Random Forest (SSL-RF) extends the classic Random Forest by exploiting both labeled and unlabeled training examples. When labeling data is expensive or time-consuming, SSL-RF assigns tentative pseudo-labels to unlabeled observations through the forest itself, then retrains on the enriched dataset, progressively improving accuracy without requiring additional human annotation.

Tabular, image-derived, or mixed; partially labeled datasets

Leistner, C., Saffari, A., Santner, J., & Bischof, H. (2009). Semi-supervised random forests. In Proceedings of the IEEE 12th International Conference on Computer Vision (ICCV), pp. 506–513. IEEE.

Zhu, X. (2005). Semi-supervised learning literature survey. Computer Sciences Technical Report 1530, University of Wisconsin-Madison.

random-forest self-training label-propagation semi-supervised-svm co-training gradient-boosting


Semi-supervised Reinforcement Learning

Semi-supervised Reinforcement Learning (SSRL)

SSRL
semi-supervised RL
RL with unlabeled data
label-efficient reinforcement learning

Multiple contributors (Laskin, Srinivas, Abbeel et al.)

Semi-supervised reinforcement learning (SSRL) combines standard reinforcement learning — where an agent learns from sparse reward signals — with semi-supervised techniques that extract structure from unlabeled environment interactions. The goal is to improve sample efficiency and generalization when reward feedback is costly, delayed, or available only for a fraction of the agent's experience.

Semi-supervised training paradigm for RL agents

Environment interactions (reward-labeled and unlabeled transitions)

Zhan, X., Zhu, X., & Shi, H. (2022). Deepthermal: Combustion optimization for thermal power generating units using offline reinforcement learning. Proceedings of the AAAI Conference on Artificial Intelligence, 36(4), 4680–4688.

Laskin, M., Srinivas, A., & Abbeel, P. (2020). CURL: Contrastive Unsupervised Representations for Reinforcement Learning. Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119, 5639–5650.


Semi-supervised RoBERTa-based Text Classification

Semi-supervised RoBERTa
RoBERTa with semi-supervised learning
SSL-RoBERTa classification
RoBERTa pseudo-label classification

Liu et al. (RoBERTa, 2019); semi-supervised adaptation by the NLP community

Semi-supervised RoBERTa-based classification combines a large pretrained RoBERTa language model with both a small labeled dataset and a larger pool of unlabeled text. By generating pseudo-labels or enforcing consistency on unlabeled examples, the method extracts supervisory signal from unannotated data, yielding stronger classifiers when ground-truth annotations are scarce.

Semi-supervised fine-tuning of a pretrained language model

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.

Xie, Q., Dai, Z., Hovy, E., Luong, M.-T., & Le, Q. V. (2020). Unsupervised Data Augmentation for Consistency Training. Advances in Neural Information Processing Systems (NeurIPS), 33, 11904–11915.


Semi-supervised Semantic Segmentation

Semi-supervised Semantic Segmentation (Pseudo-label and Consistency-based)

Semi-SSL segmentation
pseudo-label segmentation
consistency regularization segmentation
label-efficient semantic segmentation

Multiple (Ouali et al., Zou et al., Chen et al.)

Semi-supervised semantic segmentation trains pixel-level labeling models using a small set of fully labeled images combined with a much larger set of unlabeled images. Techniques such as pseudo-labeling and consistency regularization extract supervisory signal from unlabeled data, making it possible to achieve near-fully-supervised accuracy at a fraction of the annotation cost.

Semi-supervised deep learning for pixel-level classification

Ouali, Y., Hudelot, C., & Tami, M. (2020). Semi-Supervised Semantic Segmentation with Cross-Consistency Training. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12674–12684.

Zou, Y., Zhang, Z., Zhang, H., Li, C.-L., Bian, X., Huang, J.-B., & Pfister, T. (2020). PseudoSeg: Designing Pseudo Labels for Semantic Segmentation. International Conference on Learning Representations (ICLR 2021).


Semi-supervised Sentence Embeddings

Semi-supervised Sentence Embeddings (Contrastive and Self-training Approaches)

Pseudo-labeled sentence representation learning

Gao, T.; Reimers, N. et al. (multiple contributors)

Semi-supervised sentence embeddings combine a small set of labeled sentence pairs with large quantities of unlabeled text to train dense vector representations of sentences. By exploiting abundant unlabeled data through contrastive objectives or pseudo-labeling, these models produce high-quality embeddings for semantic similarity, retrieval, and classification even when annotated data is scarce.

Unlabeled and partially labeled text corpora

Gao, T., Yao, X., & Chen, D. (2021). SimCSE: Simple Contrastive Learning of Sentence Embeddings. In Proceedings of EMNLP 2021 (pp. 6894–6910). Association for Computational Linguistics.

Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of EMNLP-IJCNLP 2019 (pp. 3982–3992). Association for Computational Linguistics.


Semi-supervised Sentiment Analysis

Semi-supervised Sentiment Analysis (Label Propagation and Self-Training for Opinion Mining)

Zhu, X.; Pang, B. & Lee, L. (foundational works)

Semi-supervised sentiment analysis combines a small set of manually labeled text samples with a large pool of unlabeled text to train opinion classifiers. By propagating sentiment signals from labeled seeds to unlabeled data through self-training, label propagation, or consistency regularization, the approach achieves competitive accuracy without the cost of labeling large corpora.

Zhu, X. (2005). Semi-Supervised Learning Literature Survey. Technical Report 1530, Computer Sciences, University of Wisconsin-Madison.

Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135.


Semi-supervised Stacking Ensemble

Semi-supervised Stacking Ensemble (Self-trained Stacked Generalization)

SSL stacking
semi-supervised stacked generalization
self-trained stacking
semi-supervised meta-learning ensemble

Combines Wolpert (1992) stacking with semi-supervised learning principles

Semi-supervised Stacking Ensemble extends the classic stacked generalization framework to settings where only a fraction of training examples carry labels. Base learners are first trained on labeled data, then used to assign pseudo-labels to unlabeled examples; the expanded dataset trains stronger base models whose out-of-fold predictions form the input to a meta-learner, yielding a two-tier ensemble that exploits both labeled and unlabeled structure.

Ensemble (stacked generalization with unlabeled data augmentation)

Partially labeled tabular or feature-vector data

Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.

Chapelle, O., Schölkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press.

stacking-ensemble self-training label-propagation random-forest gradient-boosting bagging-ensemble


Semi-supervised Support Vector Machine

Semi-supervised Support Vector Machine (S3VM / Transductive SVM)

Semi-supervised Support Vector Machine (S3VM) extends the classical SVM by incorporating large quantities of unlabeled data alongside a small labeled training set. It seeks a maximum-margin hyperplane that not only separates the labeled examples but also passes through low-density regions of the full data distribution, yielding better generalization when labeled samples are scarce.

Joachims, T. (1999). Transductive Inference for Text Classification using Support Vector Machines. Proceedings of the 16th International Conference on Machine Learning (ICML), 200–209.

Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press.

svm-classification label-propagation self-training graph-based-semi-supervised-learning logistic-regression random-forest


Semi-supervised Text Summarization

Semi-supervised Text Summarization (Label-efficient Abstractive and Extractive Summarization)

semi-supervised summarization
label-efficient summarization
weakly supervised summarization
SSL-based summarization

Multiple contributors (e.g., He et al., 2020; Fabbri et al.)

Semi-supervised text summarization trains summarization models by leveraging large amounts of unlabeled text alongside a small set of human-written reference summaries. By using techniques such as language-model pretraining, pseudo-labeling, and self-training, these methods substantially reduce the annotation burden while maintaining competitive ROUGE scores on benchmark datasets.

He, J., Zhou, C., Ma, X., Berg-Kirkpatrick, T., & Neubig, G. (2020). Revisiting Semi-Supervised Learning for Neural Sequence Generation. In Proceedings of ICLR 2020.

extractive-summarization abstractive-summarization bert sequence-to-sequence self-training transformer


Semi-supervised Topic Modeling

Semi-supervised Topic Modeling (Seed-guided and Labeled LDA variants)

semi-supervised LDA
labeled LDA
seed-guided topic modeling
constrained topic model

Ramage, D.; Andrzejewski, D.; and related NLP community

Semi-supervised topic modeling extends unsupervised topic models such as LDA by incorporating partial human supervision — seed words, labeled documents, or must-link/cannot-link constraints — to steer discovered topics toward meaningful, domain-relevant categories while still exploiting the large unlabeled corpus for statistical strength.

Probabilistic graphical model (supervised/constrained extension of LDA)

Ramage, D., Hall, D., Nallapati, R., & Manning, C. D. (2009). Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, 248–256. Association for Computational Linguistics.

Andrzejewski, D., Zhu, X., & Craven, M. (2009). Incorporating domain knowledge into topic modeling via Dirichlet forest priors. Proceedings of the 26th Annual International Conference on Machine Learning (ICML), 25–32.

latent-dirichlet-allocation neural-topic-model bert-text-classification word2vec non-negative-matrix-factorization transformer-language-model


Semi-supervised Transfer Learning

SSTL
semi-supervised domain adaptation
transfer learning with unlabeled data
few-label transfer learning

Pan, S. J. & Yang, Q. (formalized); wider community

Semi-supervised Transfer Learning combines knowledge transferred from a richly labeled source domain with the structure of abundant unlabeled target-domain data, using only a small set of labeled target examples to achieve strong generalization where full annotation is scarce or expensive.

Labeled + unlabeled data from source and target domains

Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., & He, Q. (2021). A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1), 43–76.

transfer-learning semi-supervised-learning self-supervised-learning domain-adaptation fine-tuning label-propagation


Semi-supervised Transformer

Semi-supervised Learning with Transformer Architectures

transformer with self-supervised pre-training

Devlin, J. et al. (BERT); broader SSL-Transformer paradigm community

Semi-supervised learning with Transformer architectures leverages large quantities of unlabeled data alongside a small labeled set to train powerful sequence models. The dominant pattern — exemplified by BERT — first pre-trains the Transformer on unlabeled data using self-supervised objectives such as masked token prediction, then fine-tunes it on the labeled task. This two-stage approach dramatically reduces the labeled data needed to achieve strong performance.

Text, sequences, images (modality-agnostic architecture)

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186.

Zoph, B., Ghiasi, G., Lin, T.-Y., Cui, Y., Liu, H., Cubuk, E. D., & Le, Q. V. (2020). Rethinking Pre-training and Self-training. Advances in Neural Information Processing Systems (NeurIPS), 33, 3833–3845.


Semi-supervised Variational Autoencoder

Semi-supervised Variational Autoencoder (M1/M2 Generative Model)

Semi-supervised VAE
M2 model
VAE with label propagation
deep generative semi-supervised model

Kingma, D. P.; Mohamed, S.; Rezende, D. J.; Wierstra, D.

The semi-supervised VAE (M2 model) is a deep generative method that jointly learns a latent representation of inputs and a classifier, leveraging both labeled and unlabeled examples in a principled probabilistic framework. Introduced by Kingma et al. in 2014, it allows accurate classification even when labels are scarce by having the generative model explain away unlabeled observations.

Generative probabilistic model (semi-supervised)

Mixed labeled and unlabeled data (images, structured, text embeddings)

Kingma, D. P., Mohamed, S., Rezende, D. J., & Wierstra, D. (2014). Semi-supervised learning with deep generative models. Advances in Neural Information Processing Systems (NeurIPS), 27, 3581–3589.

Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR 2014).


Semi-supervised Vision Transformer

Semi-supervised Vision Transformer (Semi-supervised ViT)

Semi-supervised Self-Attention Image Model

Dosovitskiy et al. (ViT); semi-supervised extensions by multiple groups (2021–2023)

Semi-supervised Vision Transformer applies the patch-based self-attention architecture of ViT to settings where only a fraction of images are labeled, exploiting large unlabeled corpora through pseudo-labeling, consistency regularization, or self-supervised pretext tasks before fine-tuning on the small labeled set. This approach achieves near-supervised accuracy even when labeled images are scarce.

Semi-supervised deep learning for image understanding

Image data (labeled + large unlabeled sets)

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. International Conference on Learning Representations (ICLR 2021).

Zhai, X., Kolesnikov, A., Houlsby, N., & Beyer, L. (2022). Scaling Vision Transformers. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12104–12113.


Semi-supervised Voting Ensemble

Semi-supervised Voting Ensemble (Agreement-based Multi-classifier with Unlabeled Data)

semi-supervised majority vote
SSL voting ensemble
co-training voting classifier
semi-supervised multi-classifier voting

Zhou, Z.-H. & Li, M. (tri-training); Blum & Mitchell (co-training)

A semi-supervised voting ensemble trains multiple classifiers on a small labeled set, then iteratively exploits unlabeled data by having the classifiers label examples they agree on, expanding the training pool until all classifiers vote jointly on test examples. It combines the label-efficiency of semi-supervised learning with the variance-reduction of majority-vote ensembles, making it valuable when annotation is costly.

Mixed labeled and unlabeled tabular or feature data

Zhou, Z.-H., & Li, M. (2005). Tri-training: Exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 17(11), 1529–1541.

Blum, A., & Mitchell, T. (1998). Combining labeled and unlabeled data with co-training. Proceedings of the 11th Annual Conference on Computational Learning Theory (COLT), 92–100.

semi-supervised-learning voting-ensemble semi-supervised-bagging boosting co-training self-supervised-learning


Semi-supervised Word2Vec

Semi-supervised Learning with Word2Vec Word Embeddings

Word2Vec with semi-supervised learning
semi-supervised word embeddings
Word2Vec SSL
unsupervised pretraining with Word2Vec

Mikolov, T. et al. (Word2Vec); semi-supervised framing via Collobert & Weston and subsequent NLP literature

Semi-supervised Word2Vec trains dense word representations on a large unlabeled corpus using Word2Vec (skip-gram or CBOW), then uses those embeddings as fixed or fine-tunable input features for a downstream classifier trained on a small labeled dataset. This two-stage process lets models benefit from abundant unlabeled text when labeled data is scarce.

Raw text (unlabeled) + small labeled text corpus

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. In Proceedings of ICLR 2013.

Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. (2011). Natural Language Processing (Almost) from Scratch. Journal of Machine Learning Research, 12, 2493–2537.


Semi-supervised XGBoost

Semi-supervised Extreme Gradient Boosting

SS-XGBoost
semi-supervised gradient boosting
pseudo-label XGBoost
label-propagation XGBoost

Chen, T. & Guestrin, C. (XGBoost); semi-supervised extension by multiple authors

Semi-supervised XGBoost extends the XGBoost gradient boosting framework to settings where only a fraction of training examples carry labels. By iteratively generating pseudo-labels for unlabeled data and retraining on the expanded set, the method extracts signal from unlabeled observations, improving generalization when labeled data are scarce.

Ensemble (semi-supervised gradient boosting)

Tabular data with partially labeled observations

Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.

Chapelle, O., Scholkopf, B. & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press.

xgboost label-propagation pseudo-labeling gradient-boosting self-training random-forest


Semiotic Analysis

semiotics
sign analysis
structural semiotics
semiological analysis

Late 19th–early 20th century (Saussure ~1906–1911; Peirce ~1867–1914); systematic application in social research from the 1960s

Ferdinand de Saussure (structural semiology); Charles Sanders Peirce (semiotic triads); Roland Barthes (applied cultural semiotics)

Semiotic analysis is a qualitative method for interpreting how signs — words, images, sounds, gestures, and objects — produce and communicate meaning within a cultural context. Drawing on the structural linguistics of Ferdinand de Saussure and the triadic sign theory of Charles Sanders Peirce, and popularised as a research tool by Roland Barthes, semiotics moves beyond surface denotation to expose the connotative and ideological meanings embedded in texts and visual culture.

Texts, images, advertisements, film, symbols, gestures, cultural artifacts, digital media

No fixed sample; typically 5–30 purposively selected texts or artifacts

Barthes, R. (1967). Elements of Semiology (trans. A. Lavers & C. Smith). Hill and Wang.

Chandler, D. (2007). Semiotics: The Basics (2nd ed.). Routledge.

discourse-analysis content-analysis narrative-analysis thematic-analysis ethnography phenomenology


Semiotics in Film Studies

Semiotic Analysis of Film and Cinema Codes

film semiotics
cinematic codes
sign analysis in cinema

Semiotics in Film Studies is a systematic method for analyzing how film produces meaning through signs, codes, and symbolic systems. Developed from linguistic semiotics and adapted to cinema by scholars like Roland Barthes, Christian Metz, and Umberto Eco, it examines how visual, auditory, and narrative elements function as signs—consisting of signifier (the form taken by the sign) and signified (the concept it represents)—to create meaning. The method reveals that cinema is not transparent communication but a complex coded system where understanding requires learning film's specific sign conventions.

Systematic method for analyzing how meaning is produced through cinematic signs and codes

Barthes, R. (1977). Image-music-text (S. Heath, Trans.). Hill and Wang.

Metz, C. (1974). Film Language: A Semiotics of the Cinema (D. J. Umiker-Sebeok, Trans.). Oxford University Press.

Stam, R. (2000). Film Theory: An Introduction. Blackwell Publishers.

Eco, U. (1976). A Theory of Semiotics. Indiana University Press.

film-narrative-analysis visual-content-analysis discourse-analysis-media auteur-theory-analysis media-framing-analysis


Sense of Belonging Scale

The Sense of Belonging Scale (SOBS) measures students' perceptions of their connectedness and acceptance within the school community. Developed by Goodenow (1993), it assesses whether students feel valued, included, and connected to peers and teachers. Sense of belonging is a critical psychological need and a powerful predictor of academic motivation, engagement, mental health, and persistence, particularly for students from underrepresented groups.

Goodenow, C. (1993). Classroom belonging among early adolescent students: relationships to motivation and achievement. Journal of Early Adolescence, 13(1), 21-43.

Walton, G. M., & Cohen, G. L. (2011). A brief social-belonging intervention improves academic and health outcomes of minority students. Science, 331(6023), 1447-1451.

student-engagement-scale school-climate-scale student-satisfaction-survey academic-motivation-scale