Model-level mixtures of experts for semantic segmentation are, in most tested settings, more robust to white-box adversarial attacks than fixed ensembles, but the advantage disappears under universal attacks.
Explaining and Harnessing Adversarial Examples,
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Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation
Model-level mixtures of experts for semantic segmentation are, in most tested settings, more robust to white-box adversarial attacks than fixed ensembles, but the advantage disappears under universal attacks.