  Vision transformers (ViTs) have recently demonstrated state-of-the-art
performance in a variety of vision tasks, replacing convolutional neural
networks (CNNs). Meanwhile, since ViT has a different architecture than CNN, it
may behave differently. To investigate the reliability of ViT, this paper
studies the behavior and robustness of ViT. We compared the robustness of CNN
and ViT by assuming various image corruptions that may appear in practical
vision tasks. We confirmed that for most image transformations, ViT showed
robustness comparable to CNN or more improved. However, for contrast
enhancement, severe performance degradations were consistently observed in ViT.
From a detailed analysis, we identified a potential problem: positional
embedding in ViT's patch embedding could work improperly when the color scale
changes. Here we claim the use of PreLayerNorm, a modified patch embedding
structure to ensure scale-invariant behavior of ViT. ViT with PreLayerNorm
showed improved robustness in various corruptions including contrast-varying
environments.
