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Despite success on a wide range of problems related to vision, generativeadversarial networks (GANs) often suffer from inferior performance due tounstable training, especially for text generation. To solve this issue, wepropose a new variational GAN training framework which enjoys superior trainingstability. Our approach is inspired by a connection of GANs and reinforcementlearning under a variational perspective. The connection leads to (1)probability ratio clipping that regularizes generator training to preventexcessively large updates, and (2) a sample re-weighting mechanism thatimproves discriminator training by downplaying bad-quality fake samples.Moreover, our variational GAN framework can provably overcome the trainingissue in many GANs that an optimal discriminator cannot provide any informativegradient to training generator. By plugging the training approach in diversestate-of-the-art GAN architectures, we obtain significantly improvedperformance over a range of tasks, including text generation, text styletransfer, and image generation.

Improving GAN Training with Probability Ratio Clipping and Sample Reweighting