Zhou Kaiyang, Liu Ziwei, Qiao Yu, Xiang Tao, Loy Chen Change
IEEE Trans Pattern Anal Mach Intell. 2023 Apr;45(4):4396-4415. doi: 10.1109/TPAMI.2022.3195549. Epub 2023 Mar 7.
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. Over the last ten years, research in DG has made great progress, leading to a broad spectrum of methodologies, e.g., those based on domain alignment, meta-learning, data augmentation, or ensemble learning, to name a few; DG has also been studied in various application areas including computer vision, speech recognition, natural language processing, medical imaging, and reinforcement learning. In this paper, for the first time a comprehensive literature review in DG is provided to summarize the developments over the past decade. Specifically, we first cover the background by formally defining DG and relating it to other relevant fields like domain adaptation and transfer learning. Then, we conduct a thorough review into existing methods and theories. Finally, we conclude this survey with insights and discussions on future research directions.
向分布外（OOD）数据的泛化是人类天生具备的一种能力，但机器要实现却颇具挑战。这是因为大多数学习算法严重依赖源/目标数据的独立同分布（i.i.d.）假设，而在实际中由于领域转移，这一假设常常被违背。领域泛化（DG）旨在通过仅使用源数据进行模型学习来实现OOD泛化。在过去十年中，DG研究取得了巨大进展，催生了广泛的方法，例如基于领域对齐、元学习、数据增强或集成学习等方法；DG也在包括计算机视觉、语音识别、自然语言处理、医学成像和强化学习等各种应用领域得到了研究。本文首次对DG进行全面的文献综述，以总结过去十年的发展情况。具体而言，我们首先通过正式定义DG并将其与领域适应和迁移学习等其他相关领域联系起来来阐述背景。然后，我们对现有方法和理论进行全面综述。最后，我们以对未来研究方向的见解和讨论结束本次综述。
