Adaptive Neighbors and Uncertainty Estimation for Source-Free Unsupervised Domain Adaptation with Noisy Labels

Unsupervised domain adaptation transfers knowledge from a labeled source domain to an unlabeled target domain, which assumes that the source domain is labeled correctly, and both source and target data are available during the adaptive process. But collecting large-scale datasets with fully precise annotations is expensive and time-consuming. Besides, due to data privacy and security issues, the source data is often inaccessible during domain adaptation and only unlabeled target data is available. Therefore, considering both the source domain data with noisy labels and the unavailability of source domain data during domain adaptation, this paper proposes an Adaptive Neighbors and Uncertainty Estimation (ANUE) method for Source-Free Unsupervised Domain Adaptation with Noisy Labels (SF-UDA-NL). To the best of our knowledge, there has been no prior research conducted on this particular issue. Specifically, since source domain data contains noisy labels, we propose a reweighted small-loss with uncertainty estimation to filter reliable samples for updating the dual-branch network. To prevent noise knowledge from misleading domain adaptation, we adopt a contrastive learning framework and design an adaptive neighbors module to help target samples generate more reliable pseudo-labels. Then, we design a reweighted contrastive loss at both class level and instance level based on uncertainty estimation to further enhance the network's classification performance. We conduct extensive experiments on three widely used datasets for unsupervised domain adaptation in image classification, and the results demonstrate the effectiveness and robustness of our method.