论文标题
无源无监督域适应的置信得分
Confidence Score for Source-Free Unsupervised Domain Adaptation
论文作者
论文摘要
无源的无监督域适应性（SFUDA）旨在使用预训练的源模型而不是源数据在未标记的目标域中获得高性能。现有的SFUDA方法为所有目标样本分配了相同的重要性，这很容易受到不正确的伪标记。为了区分样本重要性，在这项研究中，我们提出了一个新的样本置信度评分，即SFUDA的联合模型数据结构（JMDS）得分。与仅使用源或目标域知识之一的现有置信度分数不同，JMDS得分都使用了两种知识。然后，我们建议使用SFUDA的JMDS（COWA-JMDS）框架进行置信度得分调整。 COWA-JMD由JMDS分数作为样品重量和权重混合，这是我们提出的混合变体。重量混合促进该模型可以更多地利用目标域知识。实验结果表明，JMDS得分的表现优于现有的置信得分。此外，Cowa-JMDS在各种SFUDA方案：封闭，开放和部分集合的情况下实现了最先进的性能。
Source-free unsupervised domain adaptation (SFUDA) aims to obtain high performance in the unlabeled target domain using the pre-trained source model, not the source data. Existing SFUDA methods assign the same importance to all target samples, which is vulnerable to incorrect pseudo-labels. To differentiate between sample importance, in this study, we propose a novel sample-wise confidence score, the Joint Model-Data Structure (JMDS) score for SFUDA. Unlike existing confidence scores that use only one of the source or target domain knowledge, the JMDS score uses both knowledge. We then propose a Confidence score Weighting Adaptation using the JMDS (CoWA-JMDS) framework for SFUDA. CoWA-JMDS consists of the JMDS scores as sample weights and weight Mixup that is our proposed variant of Mixup. Weight Mixup promotes the model make more use of the target domain knowledge. The experimental results show that the JMDS score outperforms the existing confidence scores. Moreover, CoWA-JMDS achieves state-of-the-art performance on various SFUDA scenarios: closed, open, and partial-set scenarios.
