A Comprehensive Survey on Source-Free Domain Adaptation
University of Electronic Science and Technology of China · Shenzhen Institutes of Advanced Technology · +1 more institution
Abstract
Over the past decade, domain adaptation has become a widely studied branch of transfer learning which 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, there has been no timely and comprehensive survey in the…
Citation impact
- FWCI
- 54.36
- Percentile
- 100%
- References
- 253
Authors
5- JLJingjing LiCorresponding
University of Electronic Science and Technology of China, Shenzhen Institutes of Advanced Technology
- ZYZhiqi Yu
University of Electronic Science and Technology of China
- ZDZhekai Du
University of Electronic Science and Technology of China
- LZLei Zhu
Tongji University
- HTHeng Tao Shen
University of Electronic Science and Technology of China
Topics & keywords
- Computer science
- Domain (mathematical analysis)
- Categorization
- Adaptation (eye)
- Confidentiality
- Domain adaptation
- Transfer of learning
- Artificial intelligence