Good Semi-supervised Learning That Requires a Bad GAN

Part of Advances in Neural Information Processing Systems 30 (NIPS 2017)

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Authors

Zihang Dai, Zhilin Yang, Fan Yang, William W. Cohen, Russ R. Salakhutdinov

Abstract

<p>Semi-supervised learning methods based on generative adversarial networks (GANs) obtained strong empirical results, but it is not clear 1) how the discriminator benefits from joint training with a generator, and 2) why good semi-supervised classification performance and a good generator cannot be obtained at the same time. Theoretically we show that given the discriminator objective, good semi-supervised learning indeed requires a bad generator, and propose the definition of a preferred generator. Empirically, we derive a novel formulation based on our analysis that substantially improves over feature matching GANs, obtaining state-of-the-art results on multiple benchmark datasets.</p>