  Mixup is a procedure for data augmentation that trains networks to make
smoothly interpolated predictions between datapoints. Adversarial training is a
strong form of data augmentation that optimizes for worst-case predictions in a
compact space around each data-point, resulting in neural networks that make
much more robust predictions. In this paper, we bring these ideas together by
adversarially probing the space between datapoints, using projected gradient
descent (PGD). The fundamental approach in this work is to leverage
backpropagation through the mixup interpolation during training to optimize for
places where the network makes unsmooth and incongruous predictions.
Additionally, we also explore several modifications and nuances, like
optimization of the mixup ratio and geometrical label assignment, and discuss
their impact on enhancing network robustness. Through these ideas, we have been
able to train networks that robustly generalize better; experiments on CIFAR-10
and CIFAR-100 demonstrate consistent improvements in accuracy against strong
adversaries, including the recent strong ensemble attack AutoAttack. Our source
code would be released for reproducibility.
