  Several data augmentation methods deploy unlabeled-in-distribution (UID) data
to bridge the gap between the training and inference of neural networks.
However, these methods have clear limitations in terms of availability of UID
data and dependence of algorithms on pseudo-labels. Herein, we propose a data
augmentation method to improve generalization in both adversarial and standard
learning by using out-of-distribution (OOD) data that are devoid of the
abovementioned issues. We show how to improve generalization theoretically
using OOD data in each learning scenario and complement our theoretical
analysis with experiments on CIFAR-10, CIFAR-100, and a subset of ImageNet. The
results indicate that undesirable features are shared even among image data
that seem to have little correlation from a human point of view. We also
present the advantages of the proposed method through comparison with other
data augmentation methods, which can be used in the absence of UID data.
Furthermore, we demonstrate that the proposed method can further improve the
existing state-of-the-art adversarial training.
