  Over the past decade, domain adaptation has become a widely studied branch of
transfer learning that aims to improve performance on target domains by
leveraging knowledge from the source domain. Conventional domain adaptation
methods often assume access to both source and target domain data
simultaneously, which may not be feasible in real-world scenarios due to
privacy and confidentiality concerns. As a result, the research of Source-Free
Domain Adaptation (SFDA) has drawn growing attention in recent years, which
only utilizes the source-trained model and unlabeled target data to adapt to
the target domain. Despite the rapid explosion of SFDA work, yet there has no
timely and comprehensive survey in the field. To fill this gap, we provide a
comprehensive survey of recent advances in SFDA and organize them into a
unified categorization scheme based on the framework of transfer learning.
Instead of presenting each approach independently, we modularize several
components of each method to more clearly illustrate their relationships and
mechanics in light of the composite properties of each method. Furthermore, we
compare the results of more than 30 representative SFDA methods on three
popular classification benchmarks, namely Office-31, Office-home, and VisDA, to
explore the effectiveness of various technical routes and the combination
effects among them. Additionally, we briefly introduce the applications of SFDA
and related fields. Drawing from our analysis of the challenges facing SFDA, we
offer some insights into future research directions and potential settings.
