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Data augmentation (DA) has been widely investigated to facilitate modeloptimization in many tasks. However, in most cases, data augmentation israndomly performed for each training sample with a certain probability, whichmight incur content destruction and visual ambiguities. To eliminate this, inthis paper, we propose an effective approach, dubbed SelectAugment, to selectsamples to be augmented in a deterministic and online manner based on thesample contents and the network training status. Specifically, in each batch,we first determine the augmentation ratio, and then decide whether to augmenteach training sample under this ratio. We model this process as a two-stepMarkov decision process and adopt Hierarchical Reinforcement Learning (HRL) tolearn the augmentation policy. In this way, the negative effects of therandomness in selecting samples to augment can be effectively alleviated andthe effectiveness of DA is improved. Extensive experiments demonstrate that ourproposed SelectAugment can be adapted upon numerous commonly used DA methods,e.g., Mixup, Cutmix, AutoAugment, etc, and improve their performance onmultiple benchmark datasets of image classification and fine-grained imagerecognition.

SelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentation