Differentiable Augmentation for Data-Efficient GAN Training Shengyu Zhao IIIS, Tsinghua University and MIT Zhijian Liu MIT Ji Lin MIT Jun-Y an Zhu Adobe and CMU Song Han MIT

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.