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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 91–100 of 155 papers

TitleStatusHype
Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly DetectionCode1
R2-AD2: Detecting Anomalies by Analysing the Raw GradientCode0
Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook—0
Unseen Anomaly Detection on Networks via Multi-Hypersphere LearningCode0
Catching Both Gray and Black Swans: Open-set Supervised Anomaly DetectionCode1
Semi-supervised anomaly detection algorithm based on KL divergence (SAD-KL)—0
Learning to Adapt to Unseen Abnormal Activities under Weak SupervisionCode1
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos—0
Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame DetectionCode1
No Shifted Augmentations (NSA): compact distributions for robust self-supervised Anomaly Detection—0
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