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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 41–50 of 155 papers

TitleStatusHype
PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time SeriesCode0
SADDE: Semi-supervised Anomaly Detection with Dependable ExplanationsCode0
Take Package as Language: Anomaly Detection Using TransformerCode0
Weakly-Supervised Anomaly Detection in Surveillance Videos Based on Two-Stream I3D Convolution Network—0
Directional anomaly detection—0
MTFL: Multi-Timescale Feature Learning for Weakly-Supervised Anomaly Detection in Surveillance VideosCode0
Self-Supervised Anomaly Detection in the Wild: Favor Joint Embeddings Methods—0
Multi-Normal Prototypes Learning for Weakly Supervised Anomaly DetectionCode0
Efficient Quantum One-Class Support Vector Machines for Anomaly Detection Using Randomized Measurements and Variable Subsampling—0
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited DataCode0
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