  As clean ImageNet accuracy nears its ceiling, the research community is
increasingly more concerned about robust accuracy under distributional shifts.
While a variety of methods have been proposed to robustify neural networks,
these techniques often target models trained on ImageNet classification. At the
same time, it is a common practice to use ImageNet pretrained backbones for
downstream tasks such as object detection, semantic segmentation, and image
classification from different domains. This raises a question: Can these robust
image classifiers transfer robustness to downstream tasks? For object detection
and semantic segmentation, we find that a vanilla Swin Transformer, a variant
of Vision Transformer tailored for dense prediction tasks, transfers robustness
better than Convolutional Neural Networks that are trained to be robust to the
corrupted version of ImageNet. For CIFAR10 classification, we find that models
that are robustified for ImageNet do not retain robustness when fully
fine-tuned. These findings suggest that current robustification techniques tend
to emphasize ImageNet evaluations. Moreover, network architecture is a strong
source of robustness when we consider transfer learning.
