Single deep MoE layers improve PGD/AutoPGD robust accuracy of adversarially trained ResNets on CIFAR-100, and routing collapse under switch loss produces individual experts that are more robust than the full MoE.
Reliable evalua- tion of adversarial robustness with an ensemble of diverse parameter-free attacks
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Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers
Single deep MoE layers improve PGD/AutoPGD robust accuracy of adversarially trained ResNets on CIFAR-100, and routing collapse under switch loss produces individual experts that are more robust than the full MoE.