IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i19p3619-d932457.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/19/3619/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/19/3619/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen, Yi-Chun & Hu, Gaoji, 2020. "Learning by matching," Theoretical Economics, Econometric Society, vol. 15(1), January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Yi-Chun & Hu, Gaoji, 2024. "Bayesian stable states," Games and Economic Behavior, Elsevier, vol. 145(C), pages 102-116.
    2. Wang, Fujin & Zhao, Zhibin & Zhai, Zhi & Guo, Yanjie & Xi, Huan & Wang, Shibin & Chen, Xuefeng, 2023. "Feature disentanglement and tendency retainment with domain adaptation for Lithium-ion battery capacity estimation," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    3. Peralta, Esteban, 2024. "Not all is lost: Sorting and self-stabilizing sets," Games and Economic Behavior, Elsevier, vol. 146(C), pages 51-58.
    4. Kenny Peng & Nikhil Garg, 2024. "Wisdom and Foolishness of Noisy Matching Markets," Papers 2402.16771, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3619-:d:932457. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.