A Kernel Theory of Modern Data Augmentation

16 Mar 2018Tri DaoAlbert GuAlexander J. RatnerVirginia SmithChristopher De SaChristopher Ré

Data augmentation, a technique in which a training set is expanded with class-preserving transformations, is ubiquitous in modern machine learning pipelines. In this paper, we seek to establish a theoretical framework for understanding data augmentation... (read more)

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