Purposive Data Augmentation Strategy and Lightweight Classification Model for Small Sample Industrial Defect Dataset-Reference-Cited by-同舟云学术

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Purposive Data Augmentation Strategy and Lightweight Classification Model for Small Sample Industrial Defect Dataset

Published:2024-09 Issue:9 Volume:20 Page:11475-11484
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Lin Liyuan¹^ORCID,Zhao Shuxian¹^ORCID,Zhang Yiran¹^ORCID,Wen Aolin¹^ORCID,Zhang Shun¹^ORCID,Yan Jingpeng¹^ORCID,Wang Ying²^ORCID,Zhou Yuan³^ORCID

Affiliation:

1. College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin, China

2. Dawning Information Industry Company Ltd., Xiqing District, Tianjin, China

3. School of Electronic Information Engineering, Tianjin University, Tianjin, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/9424/10666802/10555177.pdf?arnumber=10555177

Reference30 articles.

1. Deep-Learning-Based Small Surface Defect Detection via an Exaggerated Local Variation-Based Generative Adversarial Network

2. TAS²-Net: Triple-Attention Semantic Segmentation Network for Small Surface Defect Detection

3. Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification

4. Synchronous Medical Image Augmentation framework for deep learning-based image segmentation

5. Training generative adversarial networks with limited data;Karras;Adv. Neural Inf. Process. Syst.,2020

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