HybridNet: Classification and Reconstruction
Cooperation for Semi-Supervised Learning
Abstract. In this paper, we introduce a new model for leveraging unlabeled 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 unsupervised 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 outperform 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