  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. Code is available at https://github.com/chenxi52/UPA.
