Pseudo Multi-Source Domain Generalization: Bridging the Gap Between Single and Multi-Source Domain Generalization

A new framework called Pseudo Multi-source Domain Generalization (PMDG) is proposed to enable single-source domain generalization by generating synthetic pseudo-domains through style transfer and data augmentation, allowing existing multi-source domain generalization algorithms to be applied more practically. Extensive experiments demonstrate that PMDG can match or surpass the performance of actual multi-domain training, offering valuable insights for improving model robustness across varying data distributions.