Learning to Compose Domain-Specific Transformations for Data Augmentation. Learning to Compose Domain-Specific Transformations for Data Augmentation.
Paper summary 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. 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/).
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Learning to Compose Domain-Specific Transformations for Data Augmentation.
Alexander J. Ratner and Henry R. Ehrenberg and Zeshan Hussain and Jared Dunnmon and
Neural Information Processing Systems Conference - 2017 via Local dblp
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Summary by David Stutz 3 months ago
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