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.
- Publication:
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arXiv e-prints
- Pub Date:
- May 2019
- DOI:
- 10.48550/arXiv.1905.05393
- arXiv:
- arXiv:1905.05393
- Bibcode:
- 2019arXiv190505393H
- Keywords:
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- Computer Science - Computer Vision and Pattern Recognition;
- Computer Science - Machine Learning;
- Statistics - Machine Learning
- E-Print:
- ICML 2019