  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. 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,
paving the way for future innovation in meta-learning for domain
generalization.
