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Volume 10 Issue 2
Feb.  2023

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2023  >  10(2): 305-329
W. Zhang, L. F. Deng, L. Zhang, and D. R. Wu, “A survey on negative transfer,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 2, pp. 305–329, Feb. 2023. doi: 10.1109/JAS.2022.106004
Citation: W. Zhang, L. F. Deng, L. Zhang, and D. R. Wu, “A survey on negative transfer,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 2, pp. 305–329, Feb. 2023. doi: 10.1109/JAS.2022.106004
shu

A Survey on Negative Transfer

doi: 10.1109/JAS.2022.106004
  • 1.

    Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China

  • 2.

    School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China

Funds:  This work was partially supported by the National Key Research and Development Program of China (2021ZD0201300), the Hubei Province Funds for Distinguished Young Scholars (2020CFA050), and the National Natural Science Foundation of China (61873321)
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    Abstract
  • Transfer learning (TL) utilizes data or knowledge from one or more source domains to facilitate learning in a target domain. It is particularly useful when the target domain has very few or no labeled data, due to annotation expense, privacy concerns, etc. Unfortunately, the effectiveness of TL is not always guaranteed. Negative transfer (NT), i.e., leveraging source domain data/knowledge undesirably reduces learning performance in the target domain, and has been a long-standing and challenging problem in TL. Various approaches have been proposed in the literature to address this issue. However, there does not exist a systematic survey. This paper fills this gap, by first introducing the definition of NT and its causes, and reviewing over fifty representative approaches for overcoming NT, which fall into three categories: domain similarity estimation, safe transfer, and NT mitigation. Many areas, including computer vision, bioinformatics, natural language processing, recommender systems, and robotics, that use NT mitigation strategies to facilitate positive transfers, are also reviewed. Finally, we give guidelines on NT task construction and baseline algorithms, benchmark existing TL and NT mitigation approaches on three NT-specific datasets, and point out challenges and future research directions. To ensure reproducibility, our code is publicized at https://github.com/chamwen/NT-Benchmark.

     

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    • 1 https://scikit-learn.org/
    2 https://bcmi.sjtu.edu.cn/seed/seed.html
    Wen Zhang and Lingfei Deng contributed equally to this work.
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    Highlights

    • This paper performs the first comprehensive review of approaches to overcome or mitigate negative transfer (NT), a long-standing and challenging problem in transfer learning
    • This paper provides the definition of NT and its reasons, and reviews over fifty representative approaches for overcoming/mitigating NT, according to three categories: domain similarity estimation, safe transfer, and NT mitigation. Several application areas, existing challenges, and future research directions of NT, are also discussed
    • This paper provides guidelines on NT task constructions and baseline selections, and publicizes an open-source benchmark for NT study with three datasets and over twenty algorithms

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