“Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules”, Daniel Ho, Eric Liang, Ion Stoica, Pieter Abbeel, Xi Chen2019-05-14 (active learning)⁠:

A key challenge in leveraging data augmentation for neural network training is choosing an effective augmentation policy from a large search space of candidate operations. Properly chosen augmentation policies can lead to generalization improvements; however, state-of-the-art approaches such as AutoAugment are computationally infeasible to run for the ordinary user.

In this paper, we introduce a new data augmentation algorithm, Population Based Augmentation (PBA), which generates non-stationary augmentation policy schedules instead of a fixed augmentation policy.

We show that PBA can match the performance of AutoAugment on CIFAR-10, CIFAR-100, and SVHN, with 3 orders of magnitude less overall compute. On CIFAR-10 we achieve a mean test error of 1.46%, which is a slight improvement upon the current state-of-the-art.

The code for PBA is open source and is available at Github.