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Booster-Driven Consistency Training Framework for Semi-Supervised Learning

Journal of physics. Conference series, 2023-12, Vol.2665 (1), p.12021 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/2665/1/012021

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  • Title:
    Booster-Driven Consistency Training Framework for Semi-Supervised Learning
  • Author: Ma, Menghao ; Xiong, Feng ; Xu, Ye ; Gao, Luxiong ; Yang, Huafang ; Zou, Bingyu
  • Subjects: Consistency ; Deep learning ; Effectiveness ; Feedback loops ; Networks ; Physics ; Semi-supervised learning ; Smoothness
  • Is Part Of: Journal of physics. Conference series, 2023-12, Vol.2665 (1), p.12021
  • Description: Abstract The paper proposes an inductive semi-supervised learning method, called Booster-Driven Consistency Training (BDCT). In our work, we extend consistency training by designing a “booster” module for aggregating multi-view information, preventing the networks from collapsing into each other, and constructing better targets in parallel with the existing consistency training method. By booster, we mean that the one network is treated as the booster to help the training of the other two co-trained and superior networks, “spacecraft”. Furthermore, BDCT integrates smoothness enforcing into the designed fast feedback loop to ensure the effectiveness and the robustness of training. Meanwhile, adversarial examples are exploited to maintain the diversity among the networks that are learned to be smooth on the low dimensional manifold. BDCT demonstrates satisfactory performance in comparison with state-of-the-art semi-supervised deep learning methods and extensive experiments validate the effectiveness of the “booster” module.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1742-6588
    EISSN: 1742-6596
    DOI: 10.1088/1742-6596/2665/1/012021
  • Source: Geneva Foundation Free Medical Journals at publisher websites
    Institute of Physics IOP eJournals Open Access
    ProQuest Central
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