NNG-Mix: Improving Semi-Supervised Anomaly Detection With Pseudo-Anomaly Generation

IF 10.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE transactions on neural networks and learning systems Pub Date : 2024-11-20 DOI:10.1109/tnnls.2024.3497801

Hao Dong, Gaëtan Frusque, Yue Zhao, Eleni Chatzi, Olga Fink

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NNG-Mix：利用伪异常生成改进半监督异常检测

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来源期刊

IEEE transactions on neural networks and learning systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

CiteScore

23.80

自引率

9.60%

发文量

2102

审稿时长

3-8 weeks

期刊介绍： The focus of IEEE Transactions on Neural Networks and Learning Systems is to present scholarly articles discussing the theory, design, and applications of neural networks as well as other learning systems. The journal primarily highlights technical and scientific research in this domain.