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 11–20 of 155 papers

TitleStatusHype
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly DataCode1
Diffusion Models for Medical Anomaly DetectionCode1
On Diffusion Modeling for Anomaly DetectionCode1
ProDisc-VAD: An Efficient System for Weakly-Supervised Anomaly Detection in Video Surveillance ApplicationsCode1
Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour modelsCode1
Explainable Deep Few-shot Anomaly Detection with Deviation NetworksCode1
Catching Both Gray and Black Swans: Open-set Supervised Anomaly DetectionCode1
Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly DetectionCode1
Consistency-based Self-supervised Learning for Temporal Anomaly LocalizationCode1
Learning to Adapt to Unseen Abnormal Activities under Weak SupervisionCode1
Show:102550
← PrevPage 2 of 16Next →

No leaderboard results yet.