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.

本研究系统地研究了不同数据扩增技术在GAN训练中的有效性，并提供了关于如何扩增图像以改善生成图像保真度的见解和指南。我们还发现如果在真实图像和生成图像上使用扩增技术，即使只使用原始GAN，也可以达到与最新技术成果相当的生成质量。如果结合对比损失和一致性正则化等其他扩增技术，生成图像的质量会更进一步提高。最后，我们使用一致性正则化和对比损失来提供了CIFAR-10条件生成的新的最先进结果。

GAN训练的图像增强