  Data augmentation has been an indispensable tool to improve the performance
of deep neural networks, however the augmentation can hardly transfer among
different tasks and datasets. Consequently, a recent trend is to adopt AutoML
technique to learn proper augmentation policy without extensive hand-crafted
tuning. In this paper, we propose an efficient differentiable search algorithm
called Direct Differentiable Augmentation Search (DDAS). It exploits
meta-learning with one-step gradient update and continuous relaxation to the
expected training loss for efficient search. Our DDAS can achieve efficient
augmentation search without relying on approximations such as Gumbel Softmax or
second order gradient approximation. To further reduce the adverse effect of
improper augmentations, we organize the search space into a two level
hierarchy, in which we first decide whether to apply augmentation, and then
determine the specific augmentation policy. On standard image classification
benchmarks, our DDAS achieves state-of-the-art performance and efficiency
tradeoff while reducing the search cost dramatically, e.g. 0.15 GPU hours for
CIFAR-10. In addition, we also use DDAS to search augmentation for object
detection task and achieve comparable performance with AutoAugment, while being
1000x faster.
