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HybridNet: Classification and Reconstruction Cooperation for Semi-supervised Learning
[chapter]
2018
Lecture Notes in Computer Science
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
doi:10.1007/978-3-030-01234-2_10
fatcat:c3feqncthrcyzpzhcad6gxpwie