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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 21–30 of 155 papers

TitleStatusHype
NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly GenerationCode1
On Diffusion Modeling for Anomaly DetectionCode1
Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame DetectionCode1
SAD: Semi-Supervised Anomaly Detection on Dynamic GraphsCode1
Semi-supervised Anomaly Detection using AutoEncodersCode1
SLA^2P: Self-supervised Anomaly Detection with Adversarial PerturbationCode1
Consistency-based Self-supervised Learning for Temporal Anomaly LocalizationCode1
Catching Both Gray and Black Swans: Open-set Supervised Anomaly DetectionCode1
Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour modelsCode1
Weakly Supervised Anomaly Detection: A SurveyCode1
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