  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 semisupervised 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.
