  Deep Convolutional Neural Networks (CNNs) have long been the architecture of
choice for computer vision tasks. Recently, Transformer-based architectures
like Vision Transformer (ViT) have matched or even surpassed ResNets for image
classification. However, details of the Transformer architecture -- such as the
use of non-overlapping patches -- lead one to wonder whether these networks are
as robust. In this paper, we perform an extensive study of a variety of
different measures of robustness of ViT models and compare the findings to
ResNet baselines. We investigate robustness to input perturbations as well as
robustness to model perturbations. We find that when pre-trained with a
sufficient amount of data, ViT models are at least as robust as the ResNet
counterparts on a broad range of perturbations. We also find that Transformers
are robust to the removal of almost any single layer, and that while
activations from later layers are highly correlated with each other, they
nevertheless play an important role in classification.
