Generalization to out-of-distribution (OOD) data is a capability natural to humans yet challenging for machines to reproduce . This is because most statistical learning algorithms strongly rely on the i.i.d.~assumption  while in practice the target data often come from a different distribution than the source data , known as domain shift . Domain generalization (DG) aims to achieve OOD generalization by only using source domain data for model learning. Since first introduced in 2011, research in DG has undergone a decade progress. Ten years of research in this topic have led to a broad spectrum of methodologies, e.g.,  based on domain alignment, meta-learning, data augmentation, or ensemble learning, just to name a few; and have covered various 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 ten-year development in DG . First, we cover the background by giving the problem definitions and discussing how DG is related to other fields like domain adaptation and transfer learning. Second, we conduct a thorough review into existing methods and present a taxonomy based on their methodologies and motivations. Finally, we conclude this survey with potential research directions.
Generalization to out-of-distribution (OOD) data is a capability natural to humans yet challenging for machines . This is because most statistical learning algorithms strongly rely on the i.i.d.~assumption on source/target data, while in practice the target data often come from a different distribution than the source data . 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 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 in 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.
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to reproduce
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on source/target data,
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, known as domain shift
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only using source domain
using only source
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undergone a decade progress. Ten years of
made great progresses. In particular, intensive
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have
has
A
those
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have
has
R
ten-year development in DG . First, we
developments in DG in the past decade. Specifically, we first
R
giving the problem definitions and discussing how DG is related to other
formally defining DG and relating it to other research
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taxonomy
categorization
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potential
insights and discussions on future