Standard Unsupervised Domain Adaptation (UDA) methods assume the availability of both source and target data during the adaptation. In this work, we investigate Source-free Unsupervised Domain Adaptation (SF-UDA), a specific case of UDA where a model is adapted to a target domain without access to source data. We propose a novel approach for the SF-UDA setting based on a loss reweighting strategy that brings robustness against the noise that inevitably affects the pseudo-labels. The classification loss is reweighted based on the reliability of the pseudo-labels that is measured by estimating their uncertainty. Guided by such reweighting strategy, the pseudo-labels are progressively refined by aggregating knowledge from neighbouring samples. Furthermore, a self-supervised contrastive framework is leveraged as a target space regulariser to enhance such knowledge aggregation. A novel negative pairs exclusion strategy is proposed to identify and exclude negative pairs made of samples sharing the same class, even in presence of some noise in the pseudo-labels. Our method outperforms previous methods on three major benchmarks by a large margin. We set the new SF-UDA state-of-the-art on VisDA-C and DomainNet with a performance gain of +1.8% on both benchmarks and on PACS with +12.3% in the single-source setting and +6.6% in multi-target adaptation. Additional analyses demonstrate that the proposed approach is robust to the noise, which results in significantly more accurate pseudo-labels compared to state-of-the-art approaches.

翻译：标准无监督域适应（UDA）方法假定在适应过程中源数据和目标数据均可获取。本研究探讨了无源无监督域适应（SF-UDA），即模型在无法访问源数据的情况下适应目标域的特殊UDA场景。我们提出了一种基于损失重加权策略的新方法，该策略能有效对抗伪标签中不可避免的噪声。分类损失根据伪标签的可靠性进行重加权，而可靠性通过估计其不确定性来度量。在这种重加权策略的引导下，通过聚合相邻样本的知识，伪标签被逐步精炼。此外，我们利用自监督对比框架作为目标空间正则化器以增强知识聚合过程。我们提出了一种新颖的负对排除策略，用于识别并排除由相同类别样本构成的负对，即使伪标签存在噪声时也能有效运作。我们的方法在三个主要基准测试上以显著优势超越了此前方法。我们在VisDA-C和DomainNet上分别实现了+1.8%的性能提升，在PACS单源设置下提升+12.3%，多目标适应设置下提升+6.6%，确立了新的SF-UDA最佳水平。附加分析表明，所提方法对噪声具有鲁棒性，相较于最先进方法生成了更精确的伪标签。