MixUp as Directional Adversarial Training

Understand

In this work, we explain the working mechanism of MixUp in terms of adversarial training.

• We introduce a new class of adversarial training schemes, which we refer to as directional adversarial training, or DAT.
• In a nutshell, a DAT scheme perturbs a training example in the direction of another example but keeps its original label as the training target.
• We prove that MixUp is equivalent to a special subclass of DAT, in that it has the same expected loss function and corresponds to the same optimization problem asymptotically.