  Data augmentation methods are indispensable heuristics to boost the
performance of deep neural networks, especially in image recognition tasks.
Recently, several studies have shown that augmentation strategies found by
search algorithms outperform hand-made strategies. Such methods employ
black-box search algorithms over image transformations with continuous or
discrete parameters and require a long time to obtain better strategies. In
this paper, we propose a differentiable policy search pipeline for data
augmentation, which is much faster than previous methods. We introduce
approximate gradients for several transformation operations with discrete
parameters as well as the differentiable mechanism for selecting operations. As
the objective of training, we minimize the distance between the distributions
of augmented data and the original data, which can be differentiated. We show
that our method, Faster AutoAugment, achieves significantly faster searching
than prior work without a performance drop.
