Domain Generalization Through Data Augmentation: A Survey of Methods, Applications, and Challenges

Abstract

Domain generalization (DG) has become a pivotal research area in machine learning, focusing on equipping models with the ability to generalize effectively to unseen test domains that differ from the training distribution. This capability is crucial, as real-world data frequently exhibit domain shifts that violate the assumption of independent and identically distributed (i.i.d.) data, resulting in significant declines in model performance. Among the various strategies to address domain generalization, data augmentation has garnered substantial attention as an effective approach for mitigating domain shifts and improving model robustness. In this survey, we examine the role of data augmentation in domain generalization, offering a comprehensive overview of its methods, applications, and challenges. We present a detailed taxonomy of data augmentation techniques, categorized along three dimensions: scope, nature, and training dependency. Additionally, we provide a comparative analysis of key methods, highlighting their strengths and limitations. Finally, we explore the domain-specific applications of data augmentation and analyze their effectiveness in enhancing generalization across various real-world tasks, including computer vision, NLP, speech, and robotics. We conclude by examining key challenges—such as computational cost and augmentation overfitting—and outline promising research directions, with a focus on advancing cross-modal augmentation techniques and developing standardized evaluation benchmarks.