Data Augmentation Revisited: Rethinking the Distribution Gap between Clean and Augmented Data

Understand

Data augmentation has been widely applied as an effective methodology to improve generalization in particular when training deep neural networks.

• Recently, researchers proposed a few intensive data augmentation techniques, which indeed improved accuracy, yet we notice that these methods augment data have also caused a considerable gap between clean and augmented data.
• In this paper, we revisit this problem from an analytical perspective, for which we estimate the upper-bound of expected risk using two terms, namely, empirical risk and generalization error, respectively.
• We develop an understanding of data augmentation as regularization, which highlights the major features.