  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 either distribution shift or augmentation
diversity. Inspired by these, we seek to quantify how data augmentation
improves model generalization. To this end, we introduce 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.

Machine Learning;Computer Vision and Pattern Recognition;Machine Learning