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

Wed Jun 12 02:30 PM -- 02:35 PM (PDT) @ Seaside Ballroom

A key challenge of 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 an ordinary user. In this paper, we introduce a new data augmentation algorithm, Population Based Augmentation (PBA), which generates augmentation policy schedules orders of magnitude faster than previous approaches. We show that PBA can match the performance of AutoAugment with orders of magnitude less overall compute. On CIFAR-10 we achieve a mean test error of 1.46%, which is slightly better than current state-of-the-art. The code for PBA is fully open source and will be made available.

Author Information

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

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