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同时拥有源域和目标域数据。本文研究了无源无监督域适应（SF-UDA），即UDA的一种特殊情况，其中模型在没有源域数据的情况下适应目标域。我们提出了一种针对SF-UDA设置的新方法，该方法基于损失重加权策略，增强了模型对伪标签中不可避免噪声的鲁棒性。分类损失根据伪标签的可靠性进行重加权，可靠性通过估计其不确定性来度量。在此重加权策略的引导下，通过聚合邻近样本的知识逐步优化伪标签。此外，我们利用自监督对比学习框架作为目标空间正则化器，以增强这种知识聚合。提出了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，即使在伪标签存在噪声的情况下也能有效操作。我们的方法在三个主要基准数据集上大幅超越了先前方法。我们在VisDA-C和DomainNet上均以+1.8%的性能提升，在PACS单源设置下以+12.3%的性能提升，以及在多目标适应中+6.6%的性能提升，设立了新的SF-UDA最先进水平。进一步分析表明，所提方法对噪声具有鲁棒性，相比现有最先进方法，能生成显著更准确的伪标签。