Robust Generative Adversarial Network

Understand

Generative adversarial networks (GANs) are powerful generative models, but usually suffer from instability and generalization problem which may lead to poor generations.

• Most existing works focus on stabilizing the training of the discriminator while ignoring the generalization properties.
• In this work, we aim to improve the generalization capability of GANs by promoting the local robustness within the small neighborhood of the training samples.
• We also prove that the robustness in small neighborhood of training sets can lead to better generalization.