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.

翻译：过去十年来,对域的适应已成为一个广泛研究的转让学习分支,目的是通过利用来源领域的知识,改善目标领域的绩效; 常规领域适应方法往往同时使用源数据和目标领域数据,由于隐私和保密问题,在现实世界中可能不可行; 因此,对无源域适应的研究近年来日益引起注意,仅利用源培训模式和无标签目标数据适应目标领域; 尽管SFDA工作迅速展开,但在实地没有及时和全面的调查; 为了填补这一空白,我们全面调查SFDA最近的进展,并将这些数据组织成一个基于转让学习框架的统一分类计划; 我们不独立提出每一种方法,而是将每种方法的若干组成部分组合起来,以便更清楚地说明它们之间的关系和机制,根据每种方法的综合特性进行调整; 此外,我们比较了30多个具有代表性的SFDA方法在三种流行分类基准方面的结果,即Office、Office-home和VisDA, 以探索各种技术途径的实效,以及我们从SFDA的今后应用中简要地介绍SFA的组合,我们从SFA中提出一些技术途径和未来的应用。