Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 41, Issue 8, Pages 1979-1993Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2018.2858821
Keywords
Semi-supervised learning; supervised learning; robustness; adversarial training; adversarial examples; deep learning
Funding
- New Energy and Industrial Technology Development Organization (NEDO), Japan
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We propose a new regularization method based on virtual adversarial loss: a new measure of local smoothness of the conditional label distribution given input. Virtual adversarial loss is defined as the robustness of the conditional label distribution around each input data point against local perturbation. Unlike adversarial training, our method defines the adversarial direction without label information and is hence applicable to semi-supervised learning. Because the directions in which we smooth the model are only virtually adversarial, we call our method virtual adversarial training (VAT). The computational cost of VAT is relatively low. For neural networks, the approximated gradient of virtual adversarial loss can be computed with no more than two pairs of forward- and back-propagations. In our experiments, we applied VAT to supervised and semi-supervised learning tasks on multiple benchmark datasets. With a simple enhancement of the algorithm based on the entropy minimization principle, our VATachieves state-of-the-art performance for semi-supervised learning tasks on SVHN and CIFAR-10.
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