Graph-Based Active Learning With Uncertainty and Representativeness for Industrial Anomaly Detection

Author:

Xiao Kanhong1ORCID,Cao Jiangzhong1ORCID,Zeng Zekai1ORCID,Ling Wing-Kuen1ORCID

Affiliation:

1. School of Information Engineering, Guangdong University of Technology, Guangzhou, China

Funder

Joint Fund of the National Natural Science Foundation of China and Guangdong Province

Guangdong Provincial Key Laboratory of Intellectual Property and Big Data

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Reference74 articles.

1. LOF

2. Semi-supervised learning using Gaussian fields and harmonic functions;zhu;Proc 20th Int Conf Mach Learn (ICML),2003

3. Deep semi-supervised anomaly detection;ruff;arXiv 1906 02694,2019

4. Active learning for word sense disambiguation with methods for addressing the class imbalance problem;zhu;Proc Joint Conf Empirical Methods Natural Lang Process Comput Natural Lang Learn (EMNLP-CoNLL),2007

5. Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection

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