Source-Free Unsupervised Domain Adaptation: Current research and future directions

In the field of Transfer Learning, Source-Free Unsupervised Domain Adaptation (SFUDA) emerges as a practical and novel task that enables a pre-trained model to adapt to a new unlabeled domain without access to the original training data. The advancement of SFUDA has profoundly reshaped the algorithmic design of domain adaptation methods. Given the novelty and limited exploration of SFUDA, conducting a comprehensive survey is imperative to showcase methodological advancements, identify existing gaps, and uncover potential trends in this field. This paper provides an extensive review of SFUDA, encompassing methods and applications. First, based on the learning objectives during adaptation, different SFUDA methods fall into three categories: (i) Self-Tuning, (ii) Feature Alignment, and (iii) Sample Generation, with further sub-categorization within each category. Additionally, the strengths and limitations of each category are discussed, and various application areas where SFUDA can yield significant benefits are summarized. Finally, drawing from extensive observations and insights, potential future directions for SFUDA research are analyzed, with a focus on identifying emerging trends and key areas for further exploration.