Flexible and Diverse Contrastive Learning for Steel Surface Defect Recognition With Few Labeled Samples-Reference-Cited by-同舟云学术

Flexible and Diverse Contrastive Learning for Steel Surface Defect Recognition With Few Labeled Samples

Published:2023 Issue: Volume:72 Page:1-14
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Xu Ying¹^ORCID,Chen Jiacong¹^ORCID,Liang Yanyang¹^ORCID,Zhai Yikui¹^ORCID,Ying Zilu¹^ORCID,Zhou Wenlve¹^ORCID,Genovese Angelo²^ORCID,Piuri Vincenzo²^ORCID,Scotti Fabio²^ORCID

Affiliation:

1. Department of Intelligent Manufacturing, Wuyi University, Jiangmen, China

2. Depatimento di Information, Università degli Studi di Milano, Milan, Italy

Funder

Key Research Projects for Universities of Guangdong Provincial Education Department

Basic and Applied Basic Research Foundation of Guangdong Province

Guangdong, Hong Kong, Macau, and the Greater Bay Area International Science and Technology Innovation Cooperation Project

Jiangmen City Basic and Applied Basic Research Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/10146014.pdf?arnumber=10146014

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5. Semi-supervised defect classification of steel surface based on multi-training and generative adversarial network

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