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 1476–1500 of 4856 papers

TitleStatusHype
DeepGuard: A Framework for Safeguarding Autonomous Driving Systems from Inconsistent Behavior—0
Anticipated Network Surveillance -- An extrapolated study to predict cyber-attacks using Machine Learning and Data Analytics—0
DeCorus: Hierarchical Multivariate Anomaly Detection at Cloud-Scale—0
Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics—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
A Comparison of Event Representations in DEFT—0
Deep Learning-based ECG Classification on Raspberry PI using a Tensorflow Lite Model based on PTB-XL Dataset—0
Decentralized Federated Learning Preserves Model and Data Privacy—0
Deep Learning for Anomaly Detection: A Review—0
ANVIL: Anomaly-based Vulnerability Identification without Labelled Training Data—0
Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approach—0
An On-Device Federated Learning Approach for Cooperative Model Update between Edge Devices—0
Decentralized Collaborative Learning Framework with External Privacy Leakage Analysis—0
Deep Learning for Medical Anomaly Detection -- A Survey—0
Deep Learning for Medical Image Analysis—0
Dealing with Imbalanced Classes in Bot-IoT Dataset—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
AnomMAN: Detect Anomaly on Multi-view Attributed Networks—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