Intelligent Fault Diagnosis With Noisy Labels via Semisupervised Learning on Industrial Time Series-Reference-Cited by-同舟云学术

Intelligent Fault Diagnosis With Noisy Labels via Semisupervised Learning on Industrial Time Series

Published:2023-06 Issue:6 Volume:19 Page:7724-7732
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Cheng Cheng¹^ORCID,Liu Xiaoyu¹,Zhou Beitong¹,Yuan Ye¹^ORCID

Affiliation:

1. MOE Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China

Funder

National Natural Science Foundation of China

National Key R&D Program of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9424/10133897/10028769.pdf?arnumber=10028769

Reference34 articles.

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3. Understanding and improving early stopping for learning with noisy labels;bai;Proc Adv Neural Inf Process Syst,0

4. Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling;zhang;Proc Adv Neural Inf Process Syst,0

5. A closer look at memorization in deep networks;arpit;Proc Int Conf Mach Learn,0

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