Deep neural networks (DNNs) may suffer from significantly degenerated
performance when the training and test data are of different underlying
distributions. Despite the importance of model generalization to
out-of-distribution (OOD) data, the accuracy of state-of-the-art (SOTA) models
on OOD data can plummet. Recent work has demonstrated that regular or
off-manifold adversarial examples, as a special case of data augmentation, can
be used to improve OOD generalization. Inspired by this, we theoretically prove
that on-manifold adversarial examples can better benefit OOD generalization.
Nevertheless, it is nontrivial to generate on-manifold adversarial examples
because the real manifold is generally complex. To address this issue, we
proposed a novel method of Augmenting data with Adversarial examples via a
Wavelet module (AdvWavAug), an on-manifold adversarial data augmentation
technique that is simple to implement. In particular, we project a benign image
into a wavelet domain. With the assistance of the sparsity characteristic of
wavelet transformation, we can modify an image on the estimated data manifold.
We conduct adversarial augmentation based on AdvProp training framework.
Extensive experiments on different models and different datasets, including
ImageNet and its distorted versions, demonstrate that our method can improve
model generalization, especially on OOD data. By integrating AdvWavAug into the
training process, we have achieved SOTA results on some recent
transformer-based models.Comment: Computer Vision and Image Understanding (CVIU) [under review