Alexander J. Ratner, Henry R. Ehrenberg, Zeshan Hussain, Jared Dunnmon, Christopher Ré. Learning to Compose Domain-Specific Transformations for Data Augmentation. NIPS, 2017.

Ratner et al. train an adversarial generative network to learn domain-specific sequences of transformations useful for data augmentation. In particular, as indicated in Figure 1, the generator learns to predict sequences of user-specified transformations and the classifier is intended to distinguish the original images from the transformed ones. For training, the authors use reinforcement learning, because the transformations are not necessarily differentiable – which makes usage of the proposed method very convenient.

Figure 1: High-level illustration of the proposed method for learning data augmentation.

Also find this summary on ShortScience.org.

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