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
this https URL

本研究提出了一种名为 Differentiable Augmentation（DiffAugment）的方法，该方法通过对真假样本施加各种可导的增强以提高 GAN 的数据效率，有效地稳定训练，导致更好的收敛。实验表明，我们的方法具有一致的收益，可以在各种 GAN 架构和损失函数上进行无条件和条件生成。 DiffAugment 可以在只有 20％ 的训练数据情况下，在 CIFAR-10 和 CIFAR-100 上达到并匹配最高预测性能。