Omni-Frequency Channel-Selection Representations for Unsupervised Anomaly Detection-Reference-Cited by-同舟云学术

Omni-Frequency Channel-Selection Representations for Unsupervised Anomaly Detection

Published:2023 Issue: Volume:32 Page:4327-4340
ISSN:1057-7149
Container-title:IEEE Transactions on Image Processing
language:
Short-container-title:IEEE Trans. on Image Process.

Author:

Liang Yufei¹,Zhang Jiangning¹^ORCID,Zhao Shiwei²^ORCID,Wu Runze²^ORCID,Liu Yong¹^ORCID,Pan Shuwen³

Affiliation:

1. Laboratory of Advanced Perception on Robotics and Intelligent Learning, College of Control Science and Engineering, Zhejiang University, Hangzhou, China

2. NetEase Fuxi AI Laboratory, Hangzhou, China

3. Discipline of Control Science and Engineering, School of Information and Electrical Engineering, Hangzhou City University, Hangzhou, China

Funder

National Natural Science Foundation of China

Key Research and Development Program of Zhejiang Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Graphics and Computer-Aided Design,Software

Link

http://xplorestaging.ieee.org/ielx7/83/9991910/10192551.pdf?arnumber=10192551

Reference67 articles.

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2. Selective Kernel Networks

3. Patch SVDD: Patch-level SVDD for anomaly detection and segmentation;yi;Proc Asian Conf Comput Vis,2020

4. GAN Ensemble for Anomaly Detection

5. Abnormal event detection in videos using generative adversarial nets

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