  Image augmentations applied during training are crucial for the
generalization performance of image classifiers. Therefore, a large body of
research has focused on finding the optimal augmentation policy for a given
task. Yet, RandAugment [2], a simple random augmentation policy, has recently
been shown to outperform existing sophisticated policies. Only Adversarial
AutoAugment (AdvAA) [11], an approach based on the idea of adversarial
training, has shown to be better than RandAugment. In this paper, we show that
random augmentations are still competitive compared to an optimal adversarial
approach, as well as to simple curricula, and conjecture that the success of
AdvAA is due to the stochasticity of the policy controller network, which
introduces a mild form of curriculum.
