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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 41–50 of 155 papers

TitleStatusHype
Weakly Supervised Anomaly Detection via Knowledge-Data Alignment—0
Semi-supervised learning via DQN for log anomaly detection—0
Hyperbolic Anomaly Detection—0
Semi-Supervised Health Index Monitoring with Feature Generation and Fusion—0
Revisiting Non-separable Binary Classification and its Applications in Anomaly DetectionCode0
NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly GenerationCode1
Weakly Supervised Anomaly Detection for Chest X-Ray ImageCode0
CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection—0
Open-Set Graph Anomaly Detection via Normal Structure Regularisation—0
Anomaly Heterogeneity Learning for Open-set Supervised Anomaly DetectionCode1
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