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Poster
Good Semi-supervised Learning That Requires a Bad GAN
Zihang Dai · Zhilin Yang · Fan Yang · William Cohen · Ruslan Salakhutdinov

Wed Dec 06 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #111
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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.

Keywords: Semi-Supervised Learning · Adversarial Networks
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Author Information

Zihang Dai (Carnegie Mellon University)
Zhilin Yang (Carnegie Mellon University)
Fan Yang (Carnegie Mellon University)
William Cohen (Google AI)
Ruslan Salakhutdinov (Carnegie Mellon University)

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