Automated Anomaly Detection for Surface Defects by Dual Generative Networks With Limited Training Data-Reference-Cited by-同舟云学术

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Automated Anomaly Detection for Surface Defects by Dual Generative Networks With Limited Training Data

Published:2024-01 Issue:1 Volume:20 Page:421-431
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Zhang Chao¹^ORCID,Dai Wenting²^ORCID,Isoni Valerio³,Sourin Alexei¹^ORCID

Affiliation:

1. School of Computer Science and Engineering, Nanyang Technological University, Singapore

2. School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China

3. Institute of Sustainability for Chemicals, Energy and Environment, A^*STAR, Singapore

Funder

Agency for Science, Technology and Research

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/ielx7/9424/10355676/10089518.pdf?arnumber=10089518

Reference36 articles.

1. Robust kernel density estimation

2. Sparse Principal Component Thermography for Subsurface Defect Detection in Composite Products

3. Surface Defect Detection Using SVM‐Based Machine Vision System with Optimized Feature

4. Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection

5. Deep Learning for Medical Anomaly Detection – A Survey

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1. Surface defect detection of strip steel based on GT-CutMix augmentation algorithm and improved DSSD model;Measurement Science and Technology;2024-07-04

2. An Efficient Anchor-Free Defect Detector With Dynamic Receptive Field and Task Alignment;IEEE Transactions on Industrial Informatics;2024-06

3. AEKD: Unsupervised auto-encoder knowledge distillation for industrial anomaly detection;Journal of Manufacturing Systems;2024-04

4. Intelligent Contour Error Compensation of Ultraprecision Machining Using Hybrid Mechanism-Data-Driven Model Assisted With IoT Framework;IEEE Transactions on Industrial Informatics;2024

5. Unsupervised Anomaly Detection to handle Imbalanced Datasets using Auto encoders for ECG signal Classification;2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC);2023-05-26

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