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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 51–60 of 155 papers

TitleStatusHype
Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategy—0
Weakly-Supervised Video Anomaly Detection with Snippet Anomalous AttentionCode0
SeMAnD: Self-Supervised Anomaly Detection in Multimodal Geospatial Datasets—0
Deep Semi-Supervised Anomaly Detection for Finding Fraud in the Futures Market—0
ADFA: Attention-augmented Differentiable top-k Feature Adaptation for Unsupervised Medical Anomaly DetectionCode0
Label-based Graph Augmentation with Metapath for Graph Anomaly DetectionCode0
Semi-Supervised Anomaly Detection for the Determination of Vehicle Hijacking Tweets—0
ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced RecognitionCode1
Future Video Prediction from a Single Frame for Video Anomaly Detection—0
Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch RetrievalCode1
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