Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
Daniel Ho · Eric Liang · Peter Chen · Ion Stoica · Pieter Abbeel

Wed Jun 12th 06:30 -- 09:00 PM @ Pacific Ballroom #134

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

Author Information

Daniel Ho (UC Berkeley)
Eric Liang (UC Berkeley)
Peter Chen (
Ion Stoica (UC Berkeley)
Pieter Abbeel (UC Berkeley)

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