12 Aug 2022 | Kaiyang Zhou, Ziwei Liu, Yu Qiao, Tao Xiang, and Chen Change Loy
This paper provides a comprehensive literature review on domain generalization (DG), a field that aims to enable machine learning models to generalize well to out-of-distribution (OOD) data. DG addresses the challenge of domain shift, where the distribution of the target data differs from the source data used for model training. The paper covers the background of DG, including its definition, problem formulation, and evaluation metrics. It then reviews various methodologies for DG, such as domain alignment, meta-learning, data augmentation, and ensemble learning. Each methodology is discussed in detail, highlighting its key concepts, techniques, and applications. The paper also discusses the relationship between DG and related fields like supervised learning, multi-task learning, transfer learning, zero-shot learning, and domain adaptation. Finally, the paper concludes with insights and future research directions, emphasizing the need for more robust and efficient methods to handle domain shift in various application areas.This paper provides a comprehensive literature review on domain generalization (DG), a field that aims to enable machine learning models to generalize well to out-of-distribution (OOD) data. DG addresses the challenge of domain shift, where the distribution of the target data differs from the source data used for model training. The paper covers the background of DG, including its definition, problem formulation, and evaluation metrics. It then reviews various methodologies for DG, such as domain alignment, meta-learning, data augmentation, and ensemble learning. Each methodology is discussed in detail, highlighting its key concepts, techniques, and applications. The paper also discusses the relationship between DG and related fields like supervised learning, multi-task learning, transfer learning, zero-shot learning, and domain adaptation. Finally, the paper concludes with insights and future research directions, emphasizing the need for more robust and efficient methods to handle domain shift in various application areas.