  Recent advances on Vision Transformer (ViT) and its improved variants have
shown that self-attention-based networks surpass traditional Convolutional
Neural Networks (CNNs) in most vision tasks. However, existing ViTs focus on
the standard accuracy and computation cost, lacking the investigation of the
intrinsic influence on model robustness and generalization. In this work, we
conduct systematic evaluation on components of ViTs in terms of their impact on
robustness to adversarial examples, common corruptions and distribution shifts.
We find some components can be harmful to robustness. By using and combining
robust components as building blocks of ViTs, we propose Robust Vision
Transformer (RVT), which is a new vision transformer and has superior
performance with strong robustness. We further propose two new plug-and-play
techniques called position-aware attention scaling and patch-wise augmentation
to augment our RVT, which we abbreviate as RVT*. The experimental results on
ImageNet and six robustness benchmarks show the advanced robustness and
generalization ability of RVT compared with previous ViTs and state-of-the-art
CNNs. Furthermore, RVT-S* also achieves Top-1 rank on multiple robustness
leaderboards including ImageNet-C and ImageNet-Sketch. The code will be
available at \url{https://github.com/alibaba/easyrobust}.
