SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–60 of 155 papers

TitleStatusHype
Efficient Quantum One-Class Support Vector Machines for Anomaly Detection Using Randomized Measurements and Variable Subsampling—0
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
Future Video Prediction from a Single Frame for Video Anomaly Detection—0
Hyperbolic Anomaly Detection—0
A Neural Network-Based On-device Learning Anomaly Detector for Edge Devices—0
Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos—0
Show:102550
← PrevPage 6 of 16Next →

No leaderboard results yet.