Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules

14 May 2019Daniel HoEric LiangIon StoicaPieter AbbeelXi Chen

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... (read more)

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