LEAD: Learning Decomposition for Source-free Universal Domain Adaptation

Sanqing Qu, Tianpei Zou, Lianghua He, Florian Röhrbein, Alois Knoll, Guang Chen, Changjun Jiang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 23334-23343

Universal Domain Adaptation (UniDA) targets knowledge transfer in the presence of both covariate and label shifts. Recently Source-free Universal Domain Adaptation (SF-UniDA) has emerged to achieve UniDA without access to source data which tends to be more practical due to data protection policies. The main challenge lies in determining whether covariate-shifted samples belong to target-private unknown categories. Existing methods tackle this either through hand-crafted thresholding or by developing time-consuming iterative clustering strategies. In this paper we propose a new idea of LEArning Decomposition (LEAD) which decouples features into source-known and -unknown components to identify target-private data. Technically LEAD initially leverages the orthogonal decomposition analysis for feature decomposition. Then LEAD builds instance-level decision boundaries to adaptively identify target-private data. Extensive experiments across various UniDA scenarios have demonstrated the effectiveness and superiority of LEAD. Notably in the OPDA scenario on VisDA dataset LEAD outperforms GLC by 3.5% overall H-score and reduces 75% time to derive pseudo-labeling decision boundaries. Besides LEAD is also appealing in that it is complementary to most existing methods. The code is available at https://github. com/ispc-lab/LEAD