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A Survey on Deep Transfer Learning and Beyond

Author

Listed:
  • 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)

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.

Suggested Citation

  • Fuchao Yu & Xianchao Xiu & Yunhui Li, 2022. "A Survey on Deep Transfer Learning and Beyond," Mathematics, MDPI, vol. 10(19), pages 1-27, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3619-:d:932457
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    References listed on IDEAS

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    1. Chen, Yi-Chun & Hu, Gaoji, 2020. "Learning by matching," Theoretical Economics, Econometric Society, vol. 15(1), January.
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    Cited by:

    1. Aixia Dong, 2023. "Analysis on the Steps of Physical Education Teaching Based on Deep Learning," International Journal of Distributed Systems and Technologies (IJDST), IGI Global, vol. 14(2), pages 1-15, January.
    2. Seyed Mahdi Miraftabzadeh & Cristian Giovanni Colombo & Michela Longo & Federica Foiadelli, 2023. "A Day-Ahead Photovoltaic Power Prediction via Transfer Learning and Deep Neural Networks," Forecasting, MDPI, vol. 5(1), pages 1-16, February.

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