IAM

Check out our latest research on adversarial robustness and generalization of deep networks.
08thAPRIL2019

READING

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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