  Robustness and compactness are two essential attributes of deep learning
models that are deployed in the real world. The goals of robustness and
compactness may seem to be at odds, since robustness requires generalization
across domains, while the process of compression exploits specificity in one
domain. We introduce Adaptive Sharpness-Aware Pruning (AdaSAP), which unifies
these goals through the lens of network sharpness. The AdaSAP method produces
sparse networks that are robust to input variations which are unseen at
training time. We achieve this by strategically incorporating weight
perturbations in order to optimize the loss landscape. This allows the model to
be both primed for pruning and regularized for improved robustness. AdaSAP
improves the robust accuracy of pruned models on image classification by up to
+6% on ImageNet C and +4% on ImageNet V2, and on object detection by +4% on a
corrupted Pascal VOC dataset, over a wide range of compression ratios, pruning
criteria, and network architectures, outperforming recent pruning art by large
margins.
