Diversifying Tire-Defect Image Generation Based on Generative Adversarial Network-Reference-Cited by-同舟云学术

Diversifying Tire-Defect Image Generation Based on Generative Adversarial Network

Published:2022 Issue: Volume:71 Page:1-12
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Zhang Yulong¹^ORCID,Wang Yilin¹^ORCID,Jiang Zhiqiang²^ORCID,Liao Fagen²^ORCID,Zheng Li¹^ORCID,Tan Dongzeng¹^ORCID,Chen Jinshui¹^ORCID,Lu Jiangang¹^ORCID

Affiliation:

1. State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China

2. Zhongce Rubber Group Company, Ltd., Hangzhou, China

Funder

National Key Research and Development Plan of China

Fundamental Research Funds for the Central Universities [Zhejiang University New Generation Industrial Control System (NGICS) Platform]

Major Scientific Project of Zhejiang Laboratory

Zhejiang University Robotics Institute (Yuyao) Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/9717300/09737513.pdf?arnumber=9737513

Reference41 articles.

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5. Infogan: Interpretable representation learning by information maximizing generative adversarial nets;chen;Proc Conf Neural Inf Process Syst,2016

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