SOTAVerified

Anomaly Detection

Anomaly Detection is a binary classification identifying unusual or unexpected patterns in a dataset, which deviate significantly from the majority of the data. The goal of anomaly detection is to identify such anomalies, which could represent errors, fraud, or other types of unusual events, and flag them for further investigation.

[Image source]: GAN-based Anomaly Detection in Imbalance Problems

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4651–4675 of 4856 papers

TitleStatusHype
General Anomaly Detection of Underwater Gliders Validated by Large-scale Deployment Datasets—0
Generalizable Industrial Visual Anomaly Detection with Self-Induction Vision Transformer—0
Generalization of feature embeddings transferred from different video anomaly detection domains—0
Multi-Class Anomaly Detection—0
Generalized One-Class Learning Using Pairs of Complementary Classifiers—0
Generalizing Fault Detection Against Domain Shifts Using Stratification-Aware Cross-Validation—0
Generalizing Information to the Evolution of Rational Belief—0
General Time-series Model for Universal Knowledge Representation of Multivariate Time-Series data—0
Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection—0
Generating Anomalies for Video Anomaly Detection With Prompt-Based Feature Mapping—0
Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection—0
Generating Synthetic X-ray Images of a Person from the Surface Geometry—0
Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection—0
Generation of Anonymous Chest Radiographs Using Latent Diffusion Models for Training Thoracic Abnormality Classification Systems—0
Generation of Granular-Balls for Clustering Based on the Principle of Justifiable Granularity—0
Generative Adversarial Evasion and Out-of-Distribution Detection for UAV Cyber-Attacks—0
Generative Adversarial Networks and Other Generative Models—0
Generative AI Enabled Robust Sensor Placement in Cyber-Physical Power Systems: A Graph Diffusion Approach—0
Generative Anomaly Detection for Time Series Datasets—0
Generative Cooperative Learning for Unsupervised Video Anomaly Detection—0
Generative Damage Learning for Concrete Aging Detection using Auto-flight Images—0
Deep Generative Design: Integration of Topology Optimization and Generative Models—0
Generative Models for Anomaly Detection and Design-Space Dimensionality Reduction in Shape Optimization—0
Generative Pre-Trained Transformer for Cardiac Abnormality Detection—0
Generative Pretraining at Scale: Transformer-Based Encoding of Transactional Behavior for Fraud Detection—0
Show:102550
← PrevPage 187 of 195Next →
All datasetsMVTec ADVisAMVTec LOCO ADOne-class CIFAR-10CUHK AvenueShanghaiTechFishyscapes L&FUCR Anomaly ArchiveMPDDBTADOne-class CIFAR-100UBnormal

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016—Unverified
2CPR-fast(TensorRT)FPS362—Unverified
3CPR(TensorRT)FPS130—Unverified
4GLASSDetection AUROC99.9—Unverified
5UniNetDetection AUROC99.9—Unverified
6HETMMDetection AUROC99.8—Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9DDADDetection AUROC99.8—Unverified
10PBASDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9—Unverified
5DDADDetection AUROC98.9—Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
7DiffusionADDetection AUROC98.8—Unverified
8GLASSDetection AUROC98.8—Unverified
9TransFusionDetection AUROC98.7—Unverified
10HETMMDetection AUROC98.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3—Unverified
2PSADAvg. Detection AUROC94.9—Unverified