|
 
|
[RTC18] HybridNet: Classification and Reconstruction Cooperation for Semi-Supervised Learning
Conférence Internationale avec comité de lecture : European Conference on Computer Vision (ECCV), September 2018, pp.158-175, Munich, Germany, (DOI: https://doi.org/10.1007/978-3-030-01234-2_10)Mots clés: Deep Learning, Semi-Supervised Learning, Reconstruction and Classification, Regularization, Invariance and Stability
Résumé:
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 be-
havior 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 stud-
ies validate our contributions and the behavior of the model on both
CIFAR-10 and STL-10 datasets.
BibTeX
| @inproceedings { | |||
| RTC18, | |||
| title | = | "{HybridNet: Classification and Reconstruction Cooperation for Semi-Supervised Learning}", | |
| author | = | " T. Robert and N. Thome and M. Cord ", | |
| booktitle | = | "{European Conference on Computer Vision (ECCV)}", | |
| year | = | 2018, | |
| month | = | "September", | |
| pages | = | "158-175", | |
| address | = | "Munich, Germany", | |
| doi | = | "https://doi.org/10.1007/978-3-030-01234-2_10", | |
| } | |||
