  Generative Adversarial Networks (GANs) are known to be difficult to train,
despite considerable research effort. Several regularization techniques for
stabilizing training have been proposed, but they introduce non-trivial
computational overheads and interact poorly with existing techniques like
spectral normalization. In this work, we propose a simple, effective training
stabilizer based on the notion of consistency regularization---a popular
technique in the semi-supervised learning literature. In particular, we augment
data passing into the GAN discriminator and penalize the sensitivity of the
discriminator to these augmentations. We conduct a series of experiments to
demonstrate that consistency regularization works effectively with spectral
normalization and various GAN architectures, loss functions and optimizer
settings. Our method achieves the best FID scores for unconditional image
generation compared to other regularization methods on CIFAR-10 and CelebA.
Moreover, Our consistency regularized GAN (CR-GAN) improves state-of-the-art
FID scores for conditional generation from 14.73 to 11.48 on CIFAR-10 and from
8.73 to 6.66 on ImageNet-2012.
