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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 121–130 of 155 papers

TitleStatusHype
Elsa: Energy-based learning for semi-supervised anomaly detection—0
End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection—0
Enhanced semi-supervised stamping process monitoring with physically-informed feature extraction—0
ESAD: End-to-end Deep Semi-supervised Anomaly Detection—0
Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategy—0
Few-shot Weakly-supervised Cybersecurity Anomaly Detection—0
From Unsupervised to Semi-supervised Anomaly Detection Methods for HRRP Targets—0
Future Video Prediction from a Single Frame for Video Anomaly Detection—0
HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection—0
Hyperbolic Anomaly Detection—0
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