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Supervised Anomaly Detection

In the training set, the amount of abnormal samples is limited and significant fewer than normal samples, producing data distributions that lead to a naturally imbalanced learning problem.

Papers

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Showing 31–40 of 155 papers

TitleStatusHype
Bridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies—0
Enhanced semi-supervised stamping process monitoring with physically-informed feature extraction—0
Automated Processing of eXplainable Artificial Intelligence Outputs in Deep Learning Models for Fault Diagnostics of Large Infrastructures—0
ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects—0
A Radon-Nikodým Perspective on Anomaly Detection: Theory and Implications—0
SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection—0
Semi-supervised Anomaly Detection with Extremely Limited Labels in Dynamic Graphs—0
Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection—0
BadSAD: Clean-Label Backdoor Attacks against Deep Semi-Supervised Anomaly Detection—0
Deep evolving semi-supervised anomaly detection—0
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