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

Showing 5160 of 155 papers

TitleStatusHype
MAPL: Memory Augmentation and Pseudo-Labeling for Semi-Supervised Anomaly DetectionCode0
AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on AnomaliesCode0
How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?Code0
Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-raysCode0
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited DataCode0
IgCONDA-PET: Weakly-Supervised PET Anomaly Detection using Implicitly-Guided Attention-Conditional Counterfactual Diffusion Modeling -- a Multi-Center, Multi-Cancer, and Multi-Tracer StudyCode0
Confidence-Aware and Self-Supervised Image Anomaly LocalisationCode0
Graph Fairing Convolutional Networks for Anomaly DetectionCode0
GANomaly: Semi-Supervised Anomaly Detection via Adversarial TrainingCode0
Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly DetectionCode0
Show:102550
← PrevPage 6 of 16Next →

No leaderboard results yet.