Mihai Gabriel Constantin
Liviu-Daniel Ștefan
Bogdan Ionescu
2021-01-21
<p>While ensemble systems and late fusion mechanisms have proven their effectiveness by achieving state-of-the-art results in various computer vision tasks, current approaches are not exploiting the power of deep neural networks as their primary ensembling algorithm, but only as inducers, i.e., systems that are used as inputs for the primary ensembling algorithm. In this paper, we propose several deep neural network architectures as ensembling algorithms with various network configurations that use dense and attention layers, an input pre-processing algorithm, and a new type of deep neural network layer denoted the Cross-Space-Fusion layer, that further improves the overall results. Experimental validation is carried out on several data sets from various domains (emotional content classification, medical data captioning) and under various evaluation conditions (two-class regression, binary classification, and multi-label classification), proving the efficiency of DeepFusion.</p>
https://doi.org/10.1007/978-3-030-67832-6_20
oai:zenodo.org:5005938
eng
Zenodo
https://zenodo.org/communities/ai4media
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
MMM 2021, 27th International Conference on Multimedia Modeling, 2021, Prague, Czech Republic, June 22–24
Ensemble learning
Deep neural networks
Deep ensembles
DeepFusion: Deep Ensembles for Domain Independent System Fusion
info:eu-repo/semantics/conferencePaper