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 3676–3700 of 4856 papers

TitleStatusHype
Machine Learning-Assisted Anomaly Detection in Maritime Navigation Using AIS Data—0
Machine Learning based Anomaly Detection for 5G Networks—0
Machine Learning based Anomaly Detection for Smart Shirt: A Systematic Review—0
Machine Learning-based Anomaly Detection in Optical Fiber Monitoring—0
Machine Learning-Based Anomaly Detection of Correlated Sensor Data: An Integrated Principal Component Analysis-Autoencoder Approach—0
Machine Learning-Based Cloud Computing Compliance Process Automation—0
Machine Learning-based Layer-wise Detection of Overheating Anomaly in LPBF using Photodiode Data—0
Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things—0
Machine Learning-Based Security Policy Analysis—0
Machine Learning-based vs Deep Learning-based Anomaly Detection in Multivariate Time Series for Spacecraft Attitude Sensors—0
Machine learning-driven Anomaly Detection and Forecasting for Euclid Space Telescope Operations—0
Machine Learning for Anomaly Detection and Categorization in Multi-cloud Environments—0
Machine Learning for Anomaly Detection in Particle Physics—0
Machine Learning for Complex Systems with Abnormal Pattern by Exception Maximization Outlier Detection Method—0
Machine Learning for Drug Overdose Surveillance—0
Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations—0
Machine Learning for Real-Time Anomaly Detection in Optical Networks—0
Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey—0
Machine Learning in NextG Networks via Generative Adversarial Networks—0
Machine Learning Methods for Anomaly Detection in Nuclear Power Plant Power Transformers—0
Machine Learning with DBOS—0
Machine Learning with Memristors via Thermodynamic RAM—0
Machine Learning with Probabilistic Law Discovery: A Concise Introduction—0
MADCluster: Model-agnostic Anomaly Detection with Self-supervised Clustering Network—0
MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction—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
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