Reviews: Fast AutoAugment

While I feel that the new random baselines significantly strengthen the paper's results on CIFAR-100, random baselines are not provided for CIFAR-10, SVHN, or ImageNet. I've updated my score from a 6 to an 7, based on the random baselines for CIFAR-100 and the authors' promise to clarify their evaluation measure in the final submission. However, Cubuk et al.'s original algorithm is extremely resource-intensive. The main contribution of this paper is an algorithm that can operate on the same search space and come up with data augmentation schemes orders of magnitude more efficiently. The most closely related work I'm aware of is Population Based Augmentation (ICML 2019), which tries to solve the same problem in a different way.