HybridNet: Classification and Reconstruction Cooperation for Semi-supervised Learning

Thomas Robert 1 Nicolas Thome 2, 1 Matthieu Cord 1
1 MLIA - Machine Learning and Information Access
LIP6
2 CEDRIC - MSDMA - CEDRIC. Méthodes statistiques de data-mining et apprentissage
CEDRIC - Centre d'études et de recherche en informatique et communications
Abstract : In this paper, we introduce a new model for leveraging un-labeled data to improve generalization performances of image classifiers: a two-branch encoder-decoder architecture called HybridNet. The first branch receives supervision signal and is dedicated to the extraction of invariant class-related representations. The second branch is fully un-supervised and dedicated to model information discarded by the first branch to reconstruct input data. To further support the expected behavior of our model, we propose an original training objective. It favors stability in the discriminative branch and complementarity between the learned representations in the two branches. HybridNet is able to outper-form state-of-the-art results on CIFAR-10, SVHN and STL-10 in various semi-supervised settings. In addition, visualizations and ablation studies validate our contributions and the behavior of the model on both CIFAR-10 and STL-10 datasets.
Keywords : encoder-decoder invariance and stability re- construction regularization semi-supervised learning Deep learning
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Conference papers
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Thomas Robert, Nicolas Thome, Matthieu Cord. HybridNet: Classification and Reconstruction Cooperation for Semi-supervised Learning. ECCV 2018 - 15th European Conference on Computer Vision, Sep 2018, Munich, Germany. pp.158-175, ⟨10.1007/978-3-030-01234-2_10⟩. ⟨hal-02073640⟩

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