<p>Domain adaptation has become a widely adopted approach in machine learning due to the high costs associated with labeling data. It is typically applied when access to a labeled source domain is available. However, in real-world scenarios, privacy concerns often restrict access to sensitive information, such as fingerprints, bank account details, and facial images. A promising solution to this issue is source-free unsupervised domain adaptation (SFUDA), which enables domain adaptation without requiring access to labeled target domain data. Recent research demonstrates that SFUDA can effectively address domain discrepancies; however, two key challenges remain: (1) the low quality of prototype samples, and (2) the incorrect assignment of pseudo-labels. To tackle these challenges, we propose a method consisting of three main phases. In the first phase, we introduce a reliable sample memory (RSM) module to improve the quality of prototypes by selecting more representative samples. Since these three phases involve substantial computational costs, we leverage high-performance computing (HPC) resources and design our algorithm to have low computational complexity, enabling efficient large-scale training. In the second phase, we employ a multi-view contrastive learning (MVCL) approach to enhance pseudo-label quality by leveraging multiple data augmentations. In the final phase, we apply a noisy-label filtering technique to further refine the pseudo-labels. Our experiments on three benchmark datasets—VisDA-2017, Office-Home, and Office-31—demonstrate that our method achieves approximately 2% and 6% improvements in classification accuracy over the second-best method and the average of 13 well-known state-of-the-art approaches, respectively.</p>