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

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Showing 2276–2300 of 4856 papers

TitleStatusHype
Did You Hear That? Introducing AADG: A Framework for Generating Benchmark Data in Audio Anomaly Detection—0
Artificial intelligence for abnormality detection in high volume neuroimaging: a systematic review and meta-analysis—0
Dictionary learning approach to monitoring of wind turbine drivetrain bearings—0
Artificial Intelligence and Machine Learning in 5G Network Security: Opportunities, advantages, and future research trends—0
Diagnostics Using Nuclear Plant Cyber Attack Analysis Toolkit—0
Diagnostic Digital Twin for Anomaly Detection in Floating Offshore Wind Energy—0
Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed Anomaly Detection via Cache Lookups—0
AnoDODE: Anomaly Detection with Diffusion ODE—0
Acoustic anomaly detection via latent regularized gaussian mixture generative adversarial networks—0
Diagnosis driven Anomaly Detection for CPS—0
DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection—0
A Robust Likelihood Model for Novelty Detection—0
DFM: Interpolant-free Dual Flow Matching—0
DFM: Differentiable Feature Matching for Anomaly Detection—0
A Robust Autoencoder Ensemble-Based Approach for Anomaly Detection in Text—0
Devil in the Detail: Attack Scenarios in Industrial Applications—0
Develop End-to-End Anomaly Detection System—0
A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics—0
An LSTM-Based Predictive Monitoring Method for Data with Time-varying Variability—0
Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data—0
Determinação Automática de Limiar de Detecção de Ataques em Redes de Computadores Utilizando Autoencoders—0
A Robust and Efficient Multi-Scale Seasonal-Trend Decomposition—0
Detection of Unknown Anomalies in Streaming Videos with Generative Energy-based Boltzmann Models—0
Detection of Thin Boundaries between Different Types of Anomalies in Outlier Detection using Enhanced Neural Networks—0
A Roadmap Towards Resilient Internet of Things for Cyber-Physical Systems—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