  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
significant 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 nonstationary 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 three
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
https://github.com/arcelien/pba.
