Training GANs with Stronger Augmentations via Contrastive Discriminator

Understand

Recent works in Generative Adversarial Networks (GANs) are actively revisiting various data augmentation techniques as an effective way to prevent discriminator overfitting.

• It is still unclear, however, that which augmentations could actually improve GANs, and in particular, how to apply a wider range of augmentations in training.
• In this paper, we propose a novel way to address these questions by incorporating a recent contrastive representation learning scheme into the GAN discriminator, coined ContraD.
• This "fusion" enables the discriminators to work with much stronger augmentations without increasing their training instability, thereby preventing the discriminator overfitting issue in GANs more effectively.