  Recently, vision transformers and MLP-based models have been developed in
order to address some of the prevalent weaknesses in convolutional neural
networks. Due to the novelty of transformers being used in this domain along
with the self-attention mechanism, it remains unclear to what degree these
architectures are robust to corruptions. Despite some works proposing that data
augmentation remains essential for a model to be robust against corruptions, we
propose to explore the impact that the architecture has on corruption
robustness. We find that vision transformer architectures are inherently more
robust to corruptions than the ResNet-50 and MLP-Mixers. We also find that
vision transformers with 5 times fewer parameters than a ResNet-50 have more
shape bias. Our code is available to reproduce.

Computer Vision and Pattern Recognition;Machine Learning