A Survey on Deep Transfer Learning and Beyond

Fuchao Yu, Xianchao Xiu and Yunhui Li ()
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Fuchao Yu: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
Xianchao Xiu: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
Yunhui Li: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China

Mathematics, 2022, vol. 10, issue 19, 1-27

Abstract: Deep transfer learning (DTL), which incorporates new ideas from deep neural networks into transfer learning (TL), has achieved excellent success in computer vision, text classification, behavior recognition, and natural language processing. As a branch of machine learning, DTL applies end-to-end learning to overcome the drawback of traditional machine learning that regards each dataset individually. Although some valuable and impressive general surveys exist on TL, special attention and recent advances in DTL are lacking. In this survey, we first review more than 50 representative approaches of DTL in the last decade and systematically summarize them into four categories. In particular, we further divide each category into subcategories according to models, functions, and operation objects. In addition, we discuss recent advances in TL in other fields and unsupervised TL. Finally, we provide some possible and exciting future research directions.

Keywords: deep transfer learning (DTL); domain adaptation; machine learning; neural networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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