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Adversarial training is widely used to make classifiers robust to a specificthreat or adversary, such as $\ell_p$-norm bounded perturbations of a given$p$-norm. However, existing methods for training classifiers robust to multiplethreats require knowledge of all attacks during training and remain vulnerableto unseen distribution shifts. In this work, we describe how to obtainadversarially-robust model soups (i.e., linear combinations of parameters) thatsmoothly trade-off robustness to different $\ell_p$-norm bounded adversaries.We demonstrate that such soups allow us to control the type and level ofrobustness, and can achieve robustness to all threats without jointly trainingon all of them. In some cases, the resulting model soups are more robust to agiven $\ell_p$-norm adversary than the constituent model specialized againstthat same adversary. Finally, we show that adversarially-robust model soups canbe a viable tool to adapt to distribution shifts from a few examples.

Seasoning Model Soups for Robustness to Adversarial and Natural Distribution Shifts