On Mixup Regularization

Understand

Mixup is a data augmentation technique that creates new examples as convex combinations of training points and labels.

• This simple technique has empirically shown to improve the accuracy of many state-of-the-art models in different settings and applications, but the reasons behind this empirical success remain poorly understood.
• In this paper we take a substantial step in explaining the theoretical foundations of Mixup, by clarifying its regularization effects.
• We show that Mixup can be interpreted as standard empirical risk minimization estimator subject to a combination of data transformation and random perturbation of the transformed data.