  Data augmentation has been widely used to improve generalization in training
deep neural networks. Recent works show that using worst-case transformations
or adversarial augmentation strategies can significantly improve the accuracy
and robustness. However, due to the non-differentiable properties of image
transformations, searching algorithms such as reinforcement learning or
evolution strategy have to be applied, which are not computationally practical
for large scale problems. In this work, we show that by simply applying
consistency training with random data augmentation, state-of-the-art results on
domain adaptation (DA) and generalization (DG) can be obtained. To further
improve the accuracy and robustness with adversarial examples, we propose a
differentiable adversarial data augmentation method based on spatial
transformer networks (STN). The combined adversarial and random transformations
based method outperforms the state-of-the-art on multiple DA and DG benchmark
datasets. Besides, the proposed method shows desirable robustness to
corruption, which is also validated on commonly used datasets.
