Source-free domain adaptation aims to adapt a source-trained model to an unlabeled target domain without access to the source data. It has attracted growing attention in recent years, where existing approaches focus on self-training that usually includes pseudo-labeling techniques. In this paper, we introduce a novel noise-learning approach tailored to address noise distribution in domain adaptation settings and learn to de-confuse the pseudo-labels. More specifically, we learn a noise transition matrix of the pseudo-labels to capture the label corruption of each class and learn the underlying true label distribution. Estimating the noise transition matrix enables a better true class-posterior estimation, resulting in better prediction accuracy. We demonstrate the effectiveness of our approach when combined with several source-free domain adaptation methods: SHOT, SHOT++, and AaD. We obtain state-of-the-art results on three domain adaptation datasets: VisDA, DomainNet, and OfficeHome.

翻译：无源域适应旨在将源域训练的模型适配到无标签的目标域，且无需访问源域数据。近年来该领域受到广泛关注，现有方法主要采用包含伪标签技术的自训练策略。本文提出一种新颖的噪声学习方法，专门针对域适应场景中的噪声分布特性，通过学习实现伪标签的去混淆。具体而言，我们通过估计伪标签的噪声转移矩阵来捕捉每个类别的标签污染情况，并学习潜在的真实标签分布。噪声转移矩阵的估计能够提升真实类别后验概率的推断精度，从而获得更优的预测性能。我们将所提方法与多种无源域适应方法（SHOT、SHOT++ 和 AaD）相结合，在 VisDA、DomainNet 和 OfficeHome 这三个域适应基准数据集上取得了最先进的性能表现。