学习迁移
计算机科学
感应转移
人工智能
同种类的
领域(数学分析)
机器学习
主动学习(机器学习)
数据科学
培训转移
知识管理
机器人学习
数学
数学分析
组合数学
机器人
移动机器人
作者
Fuzhen Zhuang,Zhiyuan Qi,Keyu Duan,Dongbo Xi,Yongchun Zhu,Hengshu Zhu,Hui Xiong,Qing He
出处
期刊:Proceedings of the IEEE
[Institute of Electrical and Electronics Engineers]
日期:2020-07-07
卷期号:109 (1): 43-76
被引量:3683
标识
DOI:10.1109/jproc.2020.3004555
摘要
Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target-domain data can be reduced for constructing target learners. Due to the wide application prospects, transfer learning has become a popular and promising area in machine learning. Although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances in transfer learning. Due to the rapid expansion of the transfer learning area, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing transfer learning research studies, as well as to summarize and interpret the mechanisms and the strategies of transfer learning in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. Unlike previous surveys, this survey article reviews more than 40 representative transfer learning approaches, especially homogeneous transfer learning approaches, from the perspectives of data and model. The applications of transfer learning are also briefly introduced. In order to show the performance of different transfer learning models, over 20 representative transfer learning models are used for experiments. The models are performed on three different data sets, that is, Amazon Reviews, Reuters-21578, and Office-31, and the experimental results demonstrate the importance of selecting appropriate transfer learning models for different applications in practice.
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