  The performance of generative adversarial networks (GANs) heavily
deteriorates given a limited amount of training data. This is mainly because
the discriminator is memorizing the exact training set. To combat it, we
propose Differentiable Augmentation (DiffAugment), a simple method that
improves the data efficiency of GANs by imposing various types of
differentiable augmentations on both real and fake samples. Previous attempts
to directly augment the training data manipulate the distribution of real
images, yielding little benefit; DiffAugment enables us to adopt the
differentiable augmentation for the generated samples, effectively stabilizes
training, and leads to better convergence. Experiments demonstrate consistent
gains of our method over a variety of GAN architectures and loss functions for
both unconditional and class-conditional generation. With DiffAugment, we
achieve a state-of-the-art FID of 6.80 with an IS of 100.8 on ImageNet 128x128
and 2-4x reductions of FID given 1,000 images on FFHQ and LSUN. Furthermore,
with only 20% training data, we can match the top performance on CIFAR-10 and
CIFAR-100. Finally, our method can generate high-fidelity images using only 100
images without pre-training, while being on par with existing transfer learning
algorithms. Code is available at
https://github.com/mit-han-lab/data-efficient-gans.
