Meta-learning as a promising approach for few-shot cross-domain fault diagnosis: Algorithms, applications, and prospects

Feng, Yong; Chen, Jinglong; Xie, Jingsong; Zhang, Tianci; Lv, Haixin; Pan, Tongyang

doi:10.1016/j.knosys.2021.107646

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Meta-learning as a promising approach for few-shot cross-domain fault diagnosis: Algorithms, applications, and prospects

Author(s): Yong Feng , Jinglong Chen , Jingsong Xie , Tianci Zhang , Haixin Lv , Tongyang Pan

Publication date Created: January 2022

Publication date (Print): January 2022

Journal: Knowledge-Based Systems

Publisher: Elsevier BV

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A Survey on Transfer Learning

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Optimization Methods for Large-Scale Machine Learning

Jorge Nocedal, Frank Curtis, Léon Bottou (2018)

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Applications of machine learning to machine fault diagnosis: A review and roadmap

Yaguo Lei, Bin Yang, Xinwei Jiang … (2020)

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Journal

Title: Knowledge-Based Systems

Abbreviated Title: Knowledge-Based Systems

Publisher: Elsevier BV

ISSN (Print): 09507051

Publication date Created: January 2022

Publication date (Print): January 2022

Volume: 235

Page: 107646

Article

DOI: 10.1016/j.knosys.2021.107646

SO-VID: c6ede2dc-2bac-4335-852e-4667e57cf908

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https://www.elsevier.com/tdm/userlicense/1.0/

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Meta-learning as a promising approach for few-shot cross-domain fault diagnosis: Algorithms, applications, and prospects

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Most cited references 114

A Survey on Transfer Learning

Optimization Methods for Large-Scale Machine Learning

Applications of machine learning to machine fault diagnosis: A review and roadmap

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