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.

翻译：通用域适应（UniDA）旨在协变量偏移和标签偏移同时存在时实现知识迁移。近期兴起的无源通用域适应（SF-UniDA）能在不访问源数据的情况下完成UniDA任务，因符合数据保护政策而更具实用性。其核心挑战在于判定协变量偏移样本是否属于目标域私有未知类别。现有方法或通过人工设定阈值，或开发耗时的迭代聚类策略来应对这一问题。本文提出学习分解（LEAD）的新思路，通过将特征解耦为源已知与源未知分量以识别目标私有数据。技术上，LEAD首先利用正交分解分析进行特征解耦，随后构建实例级决策边界自适应识别目标私有数据。跨多种UniDA场景的大量实验验证了LEAD的有效性与优越性。值得注意的是，在VisDA数据集的OPDA场景中，LEAD的总体H-score较GLC提升3.5%，而伪标签决策边界生成耗时减少75%。此外，LEAD与现有方法具备良好互补性。开源代码发布于https://github.com/ispc-lab/LEAD。