RoSAS: Deep semi-supervised anomaly detection with contamination-resilient continuous supervision

• Main Authors: XU, Hongzuo, WANG, Yijie, PANG, Guansong, JIAN, Songlei, LIU, Ning, WANG, Yongjun
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Anomaly detection; Anomaly contamination; Continuous supervision; Semi-supervised learning; Deep learning; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/8267; https://ink.library.smu.edu.sg/context/sis_research/article/9270/viewcontent/Rosas_av_cc_by.pdf
• Institution: Singapore Management University

Semi-supervised anomaly detection methods leverage a few anomaly examples to yield drastically improved performance compared to unsupervised models. However, they still suffer from two limitations: 1) unlabeled anomalies (i.e., anomaly contamination) may mislead the learning process when all the unl...