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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 151–155 of 155 papers

TitleStatusHype
SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection—0
Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook—0
Self-Supervised Anomaly Detection in the Wild: Favor Joint Embeddings Methods—0
Self-Supervised Anomaly Detection of Rogue Soil Moisture Sensors—0
SeMAnD: Self-Supervised Anomaly Detection in Multimodal Geospatial Datasets—0
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