LEAD: Learning Decomposition for Source-free Universal Domain Adaptation

• URL: http://arxiv.org/abs/2403.03421v1
• Date: Wed, 6 Mar 2024 03:08:20 GMT
• Title: LEAD: Learning Decomposition for Source-free Universal Domain Adaptation
• Authors: Sanqing Qu, Tianpei Zou, Lianghua He, Florian R\"ohrbein, Alois Knoll, Guang Chen, Changjun Jiang
• Abstract summary: We propose a new idea of LEArning Decomposition, which decouples features into source-known and -unknown components to identify target-private data. 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.
• Score: 17.94547232392788
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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