Before revision:  In this work, we propose a simple and effective method to cover  much larger proportion of the attack search space, called Adversarial Data Augmentation with Mixup (MixADA ). Specifically, MixADA linearly interpolates the representations of pairs of training examples to form new virtual samples, which are more abundant and diverse than the discrete adversarial examples used in conventional ADA.
After revision:  In this work, we propose a simple and effective method to cover much larger proportion of the attack search space, called Adversarial and Mixup Data Augmentation (AMDA ). Specifically, MixADA linearly interpolates the representations of pairs of training examples to form new virtual samples, which are more abundant and diverse than the discrete adversarial examples used in conventional ADA.