RoSAS: Deep semi-supervised anomaly detection with contamination-resilient continuous supervision |
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| Institution: | 1. INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal;2. University of Coimbra, CISUC, Department of Informatics Engineering, Coimbra, Portugal;1. College of Economics, Shenzhen University, Shenzhen, Guangdong 518060, China;2. School of Management, Huazhong University of Science and Technology, Wuhan 430074, China;1. School of Business and Management, Jilin University, Changchun, China;2. Research Center for Big Data Management, Jilin University, Changchun, China;3. Department of Pediatrics, The Second Hospital of Jilin University, Changchun, China;4. Department of Information Technology & Decision Sciences, Old Dominion University, Norfolk, VA, United States |
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| Abstract: | 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 unlabeled data are employed as inliers for model training; 2) only discrete supervision information (such as binary or ordinal data labels) is exploited, which leads to suboptimal learning of anomaly scores that essentially take on a continuous distribution. Therefore, this paper proposes a novel semi-supervised anomaly detection method, which devises contamination-resilient continuous supervisory signals. Specifically, we propose a mass interpolation method to diffuse the abnormality of labeled anomalies, thereby creating new data samples labeled with continuous abnormal degrees. Meanwhile, the contaminated area can be covered by new data samples generated via combinations of data with correct labels. A feature learning-based objective is added to serve as an optimization constraint to regularize the network and further enhance the robustness w.r.t. anomaly contamination. Extensive experiments on 11 real-world datasets show that our approach significantly outperforms state-of-the-art competitors by 20%–30% in AUC-PR and obtains more robust and superior performance in settings with different anomaly contamination levels and varying numbers of labeled anomalies. |
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| Keywords: | Anomaly detection Anomaly contamination Continuous supervision Semi-supervised learning Deep learning |
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