MTHM: Self-Supervised Multitask Anomaly Detection With Hard Example Mining
Author:
Affiliation:
1. College of Computer Science and Technology and the Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
2. Hikvision Research Institute, Hangzhou, China
Funder
National Key Research and Development Program of China
Key Research and Development Program of Zhejiang
Fundamental Research Funds for the Central Universities
Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/10012124/10124756.pdf?arnumber=10124756
Reference51 articles.
1. Superpixel masking and inpainting for self-supervised anomaly detection;li;Proc Brit Mach Vis Conf,2020
2. Learning Semantic Context from Normal Samples for Unsupervised Anomaly Detection
3. CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
4. Puzzle-AE: Novelty detection in images through solving puzzles;salehi;arXiv 2008 12959,2020
5. Learning Memory-Guided Normality for Anomaly Detection
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