AEKD: Unsupervised auto-encoder knowledge distillation for industrial anomaly detection

Qiangwei Wu; Hui Li; Chenyu Tian; Long Wen; Xinyu Li

doi:10.1016/j.jmsy.2024.02.001

ScienceGate Book Chapters

JOURNAL ARTICLE

AEKD: Unsupervised auto-encoder knowledge distillation for industrial anomaly detection

Qiangwei Wu Hui Li Chenyu Tian Long Wen Xinyu Li

Year: 2024 Journal: Journal of Manufacturing Systems Vol: 73 Pages: 159-169 Publisher: Elsevier BV

DOI: 10.1016/j.jmsy.2024.02.001

Get Full-Text PDF Get Analytical Report

Keywords:

Generalization Anomaly detection Computer science Encoder Artificial intelligence Pattern recognition (psychology) Feature (linguistics) Data mining Distillation Anomaly (physics) Mathematics Physics Chemistry

Metrics

Cited By

21.08

FWCI (Field Weighted Citation Impact)

Refs

0.99

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Industrial Vision Systems and Defect Detection

Physical Sciences → Engineering → Industrial and Manufacturing Engineering

Infrastructure Maintenance and Monitoring

Physical Sciences → Engineering → Civil and Structural Engineering

AEKD: Unsupervised auto-encoder knowledge distillation for industrial anomaly detection

Metrics

Citation History

Topics

Related Documents

Unsupervised Anomaly Detection of Reciprocating Compressors using Auto-Encoder

Unsupervised Anomaly Detection Using Variational Auto-Encoder based Feature Extraction

Multitask Hybrid Knowledge Distillation for Unsupervised Anomaly Detection

Patch distance based auto-encoder for industrial anomaly detection

Efficient Industrial Anomaly Detection via Knowledge Distillation