Sungrae Park, Jun-Keon Park, Su-Jin Shin, Il-Chul Moon. Adversarial Dropout for Supervised and Semi-Supervised Learning. AAAI, 2018.

Park et al. introduce adversarial dropout, a variant of adversarial training based on adversarially computing dropout masks. Specifically, instead of training on adversarial examples, the authors propose an efficient method to compute adversarial dropout masks during training. In experiments, this approach seems to improve generalization performance in semi-supervised settings.

Also find this summary on ShortScience.org.

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