MixupE: Understanding and Improving Mixup from Directional Derivative Perspective

Understand

Mixup is a popular data augmentation technique for training deep neural networks where additional samples are generated by linearly interpolating pairs of inputs and their labels.

• This technique is known to improve the generalization performance in many learning paradigms and applications.
• In this work, we first analyze Mixup and show that it implicitly regularizes infinitely many directional derivatives of all orders.
• Based on this new insight, we propose an improved version of Mixup, theoretically justified to deliver better generalization performance than the vanilla Mixup.