Cross-Domain Semantic Transfer for Domain Generalization

Yan Wang, Hong Xie, Jinyang He, Xiaoyu Shi, Mingsheng Shang

Abstract: Data augmentation is a kind of mainstream domain generalization method aimed at enhancing the model’s ability to learn from out-of-distribution data. Most existing data augmentation methods fail to simultaneously preserve the semantic consistency and ensure the domain diversity in the augmented samples, which hinders further improvements in the generalization capacity of the model. To cope with this issue, we propose a novel cross-domain semantic transfer (CDST)-based data augmentation method, which improves generalizability from a novel perspective of exploring the diversity of semantic directions. Specifically, to ensure semantic consistency, an adjacent domain center interpolation module is proposed to find new generalization centers far from the classification boundary. To ensure data diversity, a semantic sample reproduction module is proposed to synthesize new semantic directions by transferring the cross-domain semantic information and reproduce new samples along new semantic directions around new feature centers. Furthermore, a category-preserving regularization is introduced to further constrain the category invariance of the augmented samples. Extensive experiments are implemented to verify the superiority, effectiveness, and transferability of CDST on the Digit-DG, PACS, VLCS, and OfficeHome datasets.

External IDs:dblp:journals/tomccap/WangXHSS25