Abstract. Generative adversarial networks (GANs) are powerful tools
for learning generative models. In practice, the training may suffer from
lack of convergence. GANs are commonly viewed as a two-player zerosum game between two neural networks. Here, we leverage this game
theoretic view to study the convergence behavior of the training process.
Inspired by the fictitious play learning process, a novel training method,
referred to as Fictitious GAN, is introduced. Fictitious GAN trains the
deep neural networks using a mixture of historical models. Specifically,
the discriminator (resp. generator) is updated according to the bestresponse to the mixture outputs from a sequence of previously trained
generators (resp. discriminators). It is shown that Fictitious GAN can
effectively resolve some convergence issues that cannot be resolved by the
standard training approach. It is proved that asymptotically the average
of the generator outputs has the same distribution as the data samples.