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 21–30 of 155 papers

TitleStatusHype
Multi-Normal Prototypes Learning for Weakly Supervised Anomaly DetectionCode0
Efficient Quantum One-Class Support Vector Machines for Anomaly Detection Using Randomized Measurements and Variable Subsampling—0
Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty DetectionCode0
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited DataCode0
Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization—0
Weakly-supervised anomaly detection for multimodal data distributions—0
Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Models—0
Anomaly Detection by Context Contrasting—0
Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled DataCode0
Qsco: A Quantum Scoring Module for Open-set Supervised Anomaly Detection—0
Show:102550
← PrevPage 3 of 16Next →

No leaderboard results yet.