Random augmentation can trigger gradient collisions ("evil twins") that cause forgetting; selectively averaging drifted weights with a snapshot improves single-source domain generalization.
Peer pressure: Model-to-model regularization for single source domain generalization
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Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations
Random augmentation can trigger gradient collisions ("evil twins") that cause forgetting; selectively averaging drifted weights with a snapshot improves single-source domain generalization.