LEAD: Learning Decomposition for Source-free Universal Domain Adaptation
<|reference_start|>LEAD: Learning Decomposition for Source-free Universal Domain Adaptation: 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.<|reference_end|>
@article{qu2024lead:,
    title={LEAD: Learning Decomposition for Source-free Universal Domain Adaptation},
    author={Sanqing Qu, Tianpei Zou, Lianghua He, Florian R"ohrbein, Alois Knoll,
  Guang Chen, Changjun Jiang},
    journal={arXiv preprint arXiv:2403.03421},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.03421},
    primaryClass={cs.CV cs.AI cs.LG}
}