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 4151–4175 of 4856 papers

TitleStatusHype
Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging—0
Deep Generative Models Strike Back! Improving Understanding and Evaluation in Light of Unmet Expectations for OoD Data—0
Deep Generative Model using Unregularized Score for Anomaly Detection with Heterogeneous Complexity—0
Deep Graph Learning for Anomalous Citation Detection—0
DeepGuard: A Framework for Safeguarding Autonomous Driving Systems from Inconsistent Behavior—0
Deep learning approaches to Earth Observation change detection—0
Deep Learning-based Anomaly Detection and Log Analysis for Computer Networks—0
Deep Learning-Based Anomaly Detection in Synthetic Aperture Radar Imaging—0
Deep Learning-based Anomaly Detection on X-ray Images of Fuel Cell Electrodes—0
Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses—0
Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions—0
Deep Learning-based ECG Classification on Raspberry PI using a Tensorflow Lite Model based on PTB-XL Dataset—0
Deep Learning-Driven Anomaly Detection for Green IoT Edge Networks—0
Deep Learning for Anomaly Detection: A Review—0
Deep Learning for Cross-Border Transaction Anomaly Detection in Anti-Money Laundering Systems—0
Deep Learning for Medical Anomaly Detection -- A Survey—0
Deep Learning for Medical Image Analysis—0
Deep Learning for Network Anomaly Detection under Data Contamination: Evaluating Robustness and Mitigating Performance Degradation—0
Deep Learning for Prawn Farming: Forecasting and Anomaly Detection—0
Deep Learning for Spatio-Temporal Data Mining: A Survey—0
Deep learning for structural health monitoring: An application to heritage structures—0
Deep Learning for System Trace Restoration—0
Deep Learning for the Analysis of Disruption Precursors based on Plasma Tomography—0
Deep Learning for Video Anomaly Detection: A Review—0
Deep learning guided Android malware and anomaly detection—0
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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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (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