  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. Even better, we observe that the
contrastive learning itself also benefits from our GAN training, i.e., by
maintaining discriminative features between real and fake samples, suggesting a
strong coherence between the two worlds: good contrastive representations are
also good for GAN discriminators, and vice versa. Our experimental results show
that GANs with ContraD consistently improve FID and IS compared to other recent
techniques incorporating data augmentations, still maintaining highly
discriminative features in the discriminator in terms of the linear evaluation.
Finally, as a byproduct, we also show that our GANs trained in an unsupervised
manner (without labels) can induce many conditional generative models via a
simple latent sampling, leveraging the learned features of ContraD. Code is
available at https://github.com/jh-jeong/ContraD.
