  Previous attempts for data augmentation are designed manually, and the
augmentation policies are dataset-specific. Recently, an automatic data
augmentation approach, named AutoAugment, is proposed using reinforcement
learning. AutoAugment searches for the augmentation polices in the discrete
search space, which may lead to a sub-optimal solution. In this paper, we
employ the Augmented Random Search method (ARS) to improve the performance of
AutoAugment. Our key contribution is to change the discrete search space to
continuous space, which will improve the searching performance and maintain the
diversities between sub-policies. With the proposed method, state-of-the-art
accuracies are achieved on CIFAR-10, CIFAR-100, and ImageNet (without
additional data). Our code is available at https://github.com/gmy2013/ARS-Aug.
