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Cross-Domain Feature Augmentation for Domain Generalization
May 15, 2024, 4:45 a.m. | Yingnan Liu, Yingtian Zou, Rui Qiao, Fusheng Liu, Mong Li Lee, Wynne Hsu
cs.CV updates on arXiv.org arxiv.org
Abstract: Domain generalization aims to develop models that are robust to distribution shifts. Existing methods focus on learning invariance across domains to enhance model robustness, and data augmentation has been widely used to learn invariant predictors, with most methods performing augmentation in the input space. However, augmentation in the input space has limited diversity whereas in the feature space is more versatile and has shown promising results. Nonetheless, feature semantics is seldom considered and existing feature …
abstract arxiv augmentation cs.cv data distribution domain domains feature focus however learn model robustness robust robustness space type
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