Fictitious GAN: Training GANs with Historical Models

Hao Ge, Yin Xia, Xu Chen +2

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…