Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
Abstract
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.
Cite
@article{arxiv.1905.05393,
title = {Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules},
author = {Daniel Ho and Eric Liang and Ion Stoica and Pieter Abbeel and Xi Chen},
journal= {arXiv preprint arXiv:1905.05393},
year = {2019}
}
Comments
ICML 2019