Learning to Compose Domain-Specific Transformations for Data Augmentation.Learning to Compose Domain-Specific Transformations for Data Augmentation.Alexander J. Ratner and Henry R. Ehrenberg and Zeshan Hussain and Jared Dunnmon and Christopher Ré2017
Paper summarydavidstutzRatner 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.
https://i.imgur.com/hHQkhIk.png
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/).
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.
https://i.imgur.com/hHQkhIk.png
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/).