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Automated data augmentation has shown superior performance in imagerecognition. Existing works search for dataset-level augmentation policieswithout considering individual sample variations, which are likely to besub-optimal. On the other hand, learning different policies for differentsamples naively could greatly increase the computing cost. In this paper, welearn a sample-aware data augmentation policy efficiently by formulating it asa sample reweighting problem. Specifically, an augmentation policy networktakes a transformation and the corresponding augmented image as inputs, andoutputs a weight to adjust the augmented image loss computed by a task network.At training stage, the task network minimizes the weighted losses of augmentedtraining images, while the policy network minimizes the loss of the tasknetwork on a validation set via meta-learning. We theoretically prove theconvergence of the training procedure and further derive the exact convergencerate. Superior performance is achieved on widely-used benchmarks includingCIFAR-10/100, Omniglot, and ImageNet.

MetaAugment: Sample-Aware Data Augmentation Policy Learning