  Transformers, composed of multiple self-attention layers, hold strong
promises toward a generic learning primitive applicable to different data
modalities, including the recent breakthroughs in computer vision achieving
state-of-the-art (SOTA) standard accuracy. What remains largely unexplored is
their robustness evaluation and attribution. In this work, we study the
robustness of the Vision Transformer (ViT) against common corruptions and
perturbations, distribution shifts, and natural adversarial examples. We use
six different diverse ImageNet datasets concerning robust classification to
conduct a comprehensive performance comparison of ViT models and SOTA
convolutional neural networks (CNNs), Big-Transfer. Through a series of six
systematically designed experiments, we then present analyses that provide both
quantitative and qualitative indications to explain why ViTs are indeed more
robust learners. For example, with fewer parameters and similar dataset and
pre-training combinations, ViT gives a top-1 accuracy of 28.10% on ImageNet-A
which is 4.3x higher than a comparable variant of BiT. Our analyses on image
masking, Fourier spectrum sensitivity, and spread on discrete cosine energy
spectrum reveal intriguing properties of ViT attributing to improved
robustness. Code for reproducing our experiments is available at
https://git.io/J3VO0.
