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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 11–20 of 155 papers

TitleStatusHype
On Diffusion Modeling for Anomaly DetectionCode1
SAD: Semi-Supervised Anomaly Detection on Dynamic GraphsCode1
Weakly Supervised Anomaly Detection: A SurveyCode1
Unsupervised Model Selection for Time-series Anomaly DetectionCode1
Consistency-based Self-supervised Learning for Temporal Anomaly LocalizationCode1
Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly DetectionCode1
Catching Both Gray and Black Swans: Open-set Supervised Anomaly DetectionCode1
Learning to Adapt to Unseen Abnormal Activities under Weak SupervisionCode1
Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame DetectionCode1
Diffusion Models for Medical Anomaly DetectionCode1
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