Deep neural networks (DNNs) have revolutionized artificial intelligence but often lack performance when faced with out-of-distribution (OOD) data, a common scenario due to the inevitable domain shifts in real-world applications. This limitation stems from the common assumption that training and testing data share the same distribution--an assumption frequently violated in practice. Despite their effectiveness with large amounts of data and computational power, DNNs struggle with distributional shifts and limited labeled data, leading to overfitting and poor generalization across various tasks and domains. Meta-learning presents a promising approach by employing algorithms that acquire transferable knowledge across various tasks for fast adaptation, eliminating the need to learn each task from scratch. This survey paper delves into the realm of meta-learning with a focus on its contribution to domain generalization. We first clarify the concept of meta-learning for domain generalization and introduce a novel taxonomy based on the feature extraction strategy and the classifier learning methodology, offering a granular view of methodologies. Additionally, we present a decision graph to assist readers in navigating the taxonomy based on data availability and domain shifts, enabling them to select and develop a proper model tailored to their specific problem requirements. Through an exhaustive review of existing methods and underlying theories, we map out the fundamentals of the field. Our survey provides practical insights and an informed discussion on promising research directions.

翻译：深度神经网络（DNNs）已经彻底改变了人工智能，但在面对分布外（OOD）数据时，其性能往往不足。由于现实世界应用中不可避免的领域偏移，这种情况十分常见。这一局限性源于训练数据和测试数据共享相同分布这一常见假设——该假设在实践中经常被违背。尽管DNNs在处理大量数据和计算资源时表现出色，但它们难以应对分布偏移和有限的标注数据，导致过拟合以及在各种任务和领域上泛化能力差。元学习提供了一种有前景的途径，它通过采用能够在不同任务间获取可迁移知识的算法来实现快速适应，从而无需从零开始学习每个任务。本综述论文深入探讨了元学习领域，重点关注其对领域泛化的贡献。我们首先阐明了用于领域泛化的元学习概念，并基于特征提取策略和分类器学习方法提出了一种新颖的分类法，以提供对方法论的细致观察。此外，我们提出了一个决策图，以帮助读者根据数据可用性和领域偏移情况来导航该分类法，使他们能够选择和开发适合其特定问题需求的模型。通过对现有方法及基础理论的详尽回顾，我们勾勒出了该领域的基础。我们的综述提供了实用的见解，并对有前景的研究方向进行了深入的讨论。