GCA: Geometry-aware Conditional Alignment for Partial Domain Adaptation with Coding Rate Reduction

Partial Domain Adaptation (PDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain, where the target label space is a subset of the source label space. In PDA scenario, existing methods typically achieve transferability through distribution alignment in a statistical framework, and discriminability through geometric modeling. These two aspects are often treated as separate frameworks, which severs the intrinsic connection between them. To bridge this gap, we propose a unified framework termed Geometry-aware Conditional Alignment (GCA), which is derived from theoretical insights of Maximum Coding Rate Reduction. GCA collaboratively achieves conditional alignment and orthogonal discriminability in a unified framework, making the learned features more interpretable in both statistical and geometric aspects. As a result, GCA effectively enhances both the transferability and discriminability of features. Extensive experiments on four benchmark datasets validate the effectiveness of GCA.

Authors

• Xiaohui Chen
• Chuan-Xian Ren School of Mathematics, Sun Yat-Sen University, China

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