Uncertainty-aware pseudo-label filtering for source-free unsupervised domain adaptation

Source -free unsupervised domain adaptation (SFUDA) aims to enable the utilization of a pre -trained source model in an unlabeled target domain without access to source data. Self -training is a way to solve SFUDA, where confident target samples are iteratively selected as pseudo -labeled samples to guide target model learning. However, prior heuristic noisy pseudo -label filtering methods all involve introducing extra models, which are sensitive to model assumptions and may introduce additional errors or mislabeling. In this work, we propose a method called Uncertainty -aware Pseudo -label -filtering Adaptation (UPA) to efficiently address this issue in a coarse -to -fine manner. Specially, we first introduce a sample selection module named Adaptive Pseudo -label Selection (APS), which is responsible for filtering noisy pseudo labels. The APS utilizes a simple sample uncertainty estimation method by aggregating knowledge from neighboring samples and confident samples are selected as clean pseudo -labeled. Additionally, we incorporate Class -Aware Contrastive Learning (CACL) to mitigate the memorization of pseudo -label noise by learning robust pair -wise representation supervised by pseudo labels. Through extensive experiments conducted on three widely used benchmarks, we demonstrate that our proposed method achieves competitive performance on par with state-of-the-art SFUDA methods.