Domain generalization (DG), i.e., out-of-distribution generalization, has attracted increased interests in recent years. Domain generalization deals with a challenging setting where one or several different but related domain(s) are given, and the goal is to learn a model that can generalize to an unseen test domain. For years, great progress has been achieved. This paper presents the first review for recent advances in domain generalization. First, we provide a formal definition of domain generalization and discuss several related fields. Next, we thoroughly review the theories related to domain generalization and carefully analyze the theory behind generalization. Then, we categorize recent algorithms into three classes and present them in detail: data manipulation, representation learning, and learning strategy, each of which contains several popular algorithms. Third, we introduce the commonly used datasets and applications. Finally, we summarize existing literature and present some potential research topics for the future.

中文翻译：

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泛化到看不见的域：域泛化调查

领域泛化（DG），即分布外泛化，近年来引起了越来越多的关注。域概括处理了一个具有挑战性的设置，其中给出了一个或几个不同但相关的域，目标是学习一个可以推广到一个看不见的测试域的模型。多年来，已经取得了很大的进步。本文介绍了域泛化的最新进展的第一篇综述。首先，我们提供域泛化的正式定义，并讨论几个相关领域。接下来，我们将全面回顾与领域泛化相关的理论，并仔细分析泛化背后的理论。然后，我们将最新的算法分为三类，并详细介绍它们：数据操作，表示学习和学习策略，每个算法都包含几种流行的算法。第三，我们介绍常用的数据集和应用程序。最后，我们总结了现有文献，并提出了一些未来的潜在研究主题。