  Data augmentation is an essential technique for improving generalization
ability of deep learning models. Recently, AutoAugment has been proposed as an
algorithm to automatically search for augmentation policies from a dataset and
has significantly enhanced performances on many image recognition tasks.
However, its search method requires thousands of GPU hours even for a
relatively small dataset. In this paper, we propose an algorithm called Fast
AutoAugment that finds effective augmentation policies via a more efficient
search strategy based on density matching. In comparison to AutoAugment, the
proposed algorithm speeds up the search time by orders of magnitude while
achieves comparable performances on image recognition tasks with various models
and datasets including CIFAR-10, CIFAR-100, SVHN, and ImageNet.
