Anomaly heterogeneity learning for open-set supervised anomaly detection

• Main Authors: ZHU, Jiawen, DING, Choubo, TIAN, Yu, PANG, Guansong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Anomaly detection; Machine learning; Heterogeneity learning; Heterogeneous anomaly distributions; Artificial Intelligence and Robotics; Databases and Information Systems
• 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
• Institution: Singapore Management University

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...