  Data augmentations have been widely studied to improve the accuracy and
robustness of classifiers. However, the potential of image augmentation in
improving GAN models for image synthesis has not been thoroughly investigated
in previous studies. In this work, we systematically study the effectiveness of
various existing augmentation techniques for GAN training in a variety of
settings. We provide insights and guidelines on how to augment images for both
vanilla GANs and GANs with regularizations, improving the fidelity of the
generated images substantially. Surprisingly, we find that vanilla GANs attain
generation quality on par with recent state-of-the-art results if we use
augmentations on both real and generated images. When this GAN training is
combined with other augmentation-based regularization techniques, such as
contrastive loss and consistency regularization, the augmentations further
improve the quality of generated images. We provide new state-of-the-art
results for conditional generation on CIFAR-10 with both consistency loss and
contrastive loss as additional regularizations.
