Anomaly heterogeneity learning for open-set supervised anomaly detection
Open-set supervised anomaly detection (OSAD) - a recently emerging anomaly detection area - aims at utilizing a few samples of anomaly classes seen during training to de-tect unseen anomalies (i.e., samples from open-set anomaly classes), while effectively identifying the seen anomalies. Benefiting...
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Main Authors: | ZHU, Jiawen, DING, Choubo, TIAN, Yu, PANG, Guansong |
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Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2024
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Online Access: | https://ink.library.smu.edu.sg/sis_research/9760 https://ink.library.smu.edu.sg/context/sis_research/article/10760/viewcontent/2310.12790v3.pdf |
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Institution: | Singapore Management University |
Language: | English |
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