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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 111–120 of 155 papers

TitleStatusHype
No Shifted Augmentations (NSA): compact distributions for robust self-supervised Anomaly Detection—0
LesionPaste: One-Shot Anomaly Detection for Medical Images—0
Abuse and Fraud Detection in Streaming Services Using Heuristic-Aware Machine Learning—0
Anomaly Detection in File Fragment Classification of Image File Formats—0
Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos—0
Self-Supervised Anomaly Detection by Self-Distillation and Negative SamplingCode0
A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods—0
No Shifted Augmentations (NSA): strong baselines for self-supervised Anomaly Detection—0
A Comparison of Supervised and Unsupervised Deep Learning Methods for Anomaly Detection in ImagesCode0
From Unsupervised to Semi-supervised Anomaly Detection Methods for HRRP Targets—0
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