Semantic Bridging Domains: Pseudo-Source as Test-Time Connector
Distribution shifts between training and testing data are a critical bottleneck limiting the practical utility of models, especially in real-world test-time scenarios. To adapt models when the source domain is unknown and the target domain is unlabeled, previous works constructed pseudo-source domains via data generation and translation, then aligned the target domain with them. However, significant discrepancies exist between the pseudo-source and the original source domain, leading to potential divergence when correcting the target directly. From this perspective, we propose a Stepwise Semantic Alignment (SSA) method, viewing the pseudo-source as a semantic bridge connecting the source ...
| Authors | Xizhong Yang, Huiming Wang, Ning Xu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 4 March 2026 |
| Last updated at source | 4 March 2026 |
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