Fast AutoAugment

Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, Sungwoong Kim

Abstract:

Data augmentation is an essential technique for improving generalization ability
of deep learning models. Recently, AutoAugment [3] 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.