  Despite success on a wide range of problems related to vision, generative
adversarial networks (GANs) often suffer from inferior performance due to
unstable training, especially for text generation. To solve this issue, we
propose a new variational GAN training framework which enjoys superior training
stability. Our approach is inspired by a connection of GANs and reinforcement
learning under a variational perspective. The connection leads to (1)
probability ratio clipping that regularizes generator training to prevent
excessively large updates, and (2) a sample re-weighting mechanism that
improves discriminator training by downplaying bad-quality fake samples.
Moreover, our variational GAN framework can provably overcome the training
issue in many GANs that an optimal discriminator cannot provide any informative
gradient to training generator. By plugging the training approach in diverse
state-of-the-art GAN architectures, we obtain significantly improved
performance over a range of tasks, including text generation, text style
transfer, and image generation.
