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 â€“ 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 at [davidstutz.de](https://davidstutz.de/category/reading/).