  The transformer models have shown promising effectiveness in dealing with
various vision tasks. However, compared with training Convolutional Neural
Network (CNN) models, training Vision Transformer (ViT) models is more
difficult and relies on the large-scale training set. To explain this
observation we make a hypothesis that \textit{ViT models are less effective in
capturing the high-frequency components of images than CNN models}, and verify
it by a frequency analysis. Inspired by this finding, we first investigate the
effects of existing techniques for improving ViT models from a new frequency
perspective, and find that the success of some techniques (e.g., RandAugment)
can be attributed to the better usage of the high-frequency components. Then,
to compensate for this insufficient ability of ViT models, we propose HAT,
which directly augments high-frequency components of images via adversarial
training. We show that HAT can consistently boost the performance of various
ViT models (e.g., +1.2% for ViT-B, +0.5% for Swin-B), and especially enhance
the advanced model VOLO-D5 to 87.3% that only uses ImageNet-1K data, and the
superiority can also be maintained on out-of-distribution data and transferred
to downstream tasks. The code is available at:
https://github.com/jiawangbai/HAT.
