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• W3183508146 abstract "Generalization to out-of-distribution (OOD) data is a capability natural to humans yet challenging for machines to reproduce. This is because most learning algorithms strongly rely on the i.i.d.~assumption on source/target data, which is often violated in practice due to domain shift. Domain generalization (DG) aims to achieve OOD generalization by using only source data for model learning. Since first introduced in 2011, research in DG has made great progresses. In particular, intensive research in this topic has led to a broad spectrum of methodologies, e.g., those based on domain alignment, meta-learning, data augmentation, or ensemble learning, just to name a few; and has covered various vision applications such as object recognition, segmentation, action recognition, and person re-identification. In this paper, for the first time a comprehensive literature review is provided to summarize the developments in DG for computer vision over the past decade. Specifically, we first cover the background by formally defining DG and relating it to other research fields like domain adaptation and transfer learning. Second, we conduct a thorough review into existing methods and present a categorization based on their methodologies and motivations. Finally, we conclude this survey with insights and discussions on future research directions." @default.
• W3183508146 created "2021-08-02" @default.
• W3183508146 creator A5005626854 @default.
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• W3183508146 creator A5030812622 @default.
• W3183508146 creator A5055183049 @default.
• W3183508146 creator A5064065592 @default.
• W3183508146 date "2021-03-03" @default.
• W3183508146 modified "2023-09-27" @default.
• W3183508146 title "Domain Generalization in Vision: A Survey" @default.
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