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 discriminator, coined ContraD. This "fusion" enables discriminators to work with much stronger augmentations without catastrophic forgetting, which can significantly improve GAN training. Even better, we observe that the contrastive learning itself also benefits from GAN training, i.e., keeping discriminative features between real and fake samples, suggesting a strong coherence between the two worlds: a good contrastive representation is also good for GAN discriminators, and vice versa. Our experimental results show that GAN with ContraD consistently improves FID scores compared to other recent techniques using data augmentations, still maintaining highly discriminative features in the discriminator in terms of the linear evaluation. Finally, as a byproduct, we 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.