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Data augmentation is one of the most important tools in training modern deepneural networks. Recently, great advances have been made in searching foroptimal augmentation policies in the image classification domain. However, twokey points related to data augmentation remain uncovered by the currentmethods. First is that most if not all modern augmentation search methods areoffline and learning policies are isolated from their usage. The learnedpolicies are mostly constant throughout the training process and are notadapted to the current training model state. Second, the policies rely onclass-preserving image processing functions. Hence applying current offlinemethods to new tasks may require domain knowledge to specify such kind ofoperations. In this work, we offer an orthogonal online data augmentationscheme together with three new augmentation networks, co-trained with thetarget learning task. It is both more efficient, in the sense that it does notrequire expensive offline training when entering a new domain, and moreadaptive as it adapts to the learner state. Our augmentation networks requireless domain knowledge and are easily applicable to new tasks. Extensiveexperiments demonstrate that the proposed scheme alone performs on par with thestate-of-the-art offline data augmentation methods, as well as improving uponthe state-of-the-art in combination with those methods. Code is available athttps://github.com/zhiqiangdon/online-augment .

OnlineAugment: Online Data Augmentation with Less Domain Knowledge