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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 61–70 of 155 papers

TitleStatusHype
RoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous SupervisionCode1
End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection—0
Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers—0
AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on AnomaliesCode0
On Diffusion Modeling for Anomaly DetectionCode1
AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by Random Labeling—0
SAD: Semi-Supervised Anomaly Detection on Dynamic GraphsCode1
Reconstruction Error-based Anomaly Detection with Few Outlying Examples—0
Self-Supervised Anomaly Detection of Rogue Soil Moisture Sensors—0
Weakly-Supervised Anomaly Detection in the Milky WayCode0
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