Tradeoffs in Data Augmentation: An Empirical StudyDownload PDF

Sep 28, 2020 (edited Mar 18, 2021)ICLR 2021 PosterReaders: Everyone
  • Keywords: Generalization, Interpretability, Understanding Data Augmentation
  • Abstract: Though data augmentation has become a standard component of deep neural network training, the underlying mechanism behind the effectiveness of these techniques remains poorly understood. In practice, augmentation policies are often chosen using heuristics of distribution shift or augmentation diversity. Inspired by these, we conduct an empirical study to quantify how data augmentation improves model generalization. We introduce two interpretable and easy-to-compute measures: Affinity and Diversity. We find that augmentation performance is predicted not by either of these alone but by jointly optimizing the two.
  • One-sentence Summary: We quantify mechanisms of how data augmentation works with two metrics we introduce: Affinity and Diversity.
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