The residual generator: An improved divergence minimization framework for GAN

Highlights：

• We propose a residual generator (Rg-GAN) served as a better approximation divergence minimization framework for GAN, and prove that residual generator for standard and least-squares GAN are equivalent to the minimization of reverse-KL and a new instance of f-divergence, respectively.

• We prove that Rg-GAN can be reduced to IPMs based GAN and bridge the gap between IPMs and f-divergence.

• We propose a new loss function for the discriminator of Rg-GAN that manifests a better discriminative property and therefore improved on Rg-GAN generalisation ability.

• We conduct experiments on multiple benchmark data sets and demonstrate that our proposed framework can mitigate the mode collapse issue and facilitate GAN to generate higher-quality images with negligible additional computation cost.