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 1901–1925 of 4856 papers

TitleStatusHype
Enhancing Web Service Anomaly Detection via Fine-grained Multi-modal Association and Frequency Domain Analysis—0
Autoencoder-based Condition Monitoring and Anomaly Detection Method for Rotating Machines—0
Curved Geometric Networks for Visual Anomaly Recognition—0
Ensemble and Random Collaborative Representation-Based Anomaly Detector for Hyperspectral Imagery—0
Autoencoder-based Online Data Quality Monitoring for the CMS Electromagnetic Calorimeter—0
Autoencoder-Based Detection of Anomalous Stokes V Spectra in the Flare-Producing Active Region 13663 Using Hinode/SP Observations—0
Ensemble Grammar Induction For Detecting Anomalies in Time Series—0
Ensemble Learning based Anomaly Detection for IoT Cybersecurity via Bayesian Hyperparameters Sensitivity Analysis—0
Ensemble Methods for Sequence Classification with Hidden Markov Models—0
Ensemble neuroevolution based approach for multivariate time series anomaly detection—0
Entity Embedding-based Anomaly Detection for Heterogeneous Categorical Events—0
Entropic one-class classifiers—0
Anomaly detection with semi-supervised classification based on risk estimators—0
Env-Aware Anomaly Detection: Ignore Style Changes, Stay True to Content!—0
An AI-Based Public Health Data Monitoring System—0
EPASAD: Ellipsoid decision boundary based Process-Aware Stealthy Attack Detector—0
Feature Selection for Fault Detection and Prediction based on Event Log Analysis—0
CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimation—0
An ADMM-based Optimal Transmission Frequency Management System for IoT Edge Intelligence—0
ESAD: End-to-end Deep Semi-supervised Anomaly Detection—0
Anomaly Detection with SDAE—0
Estimation of Dimensions Contributing to Detected Anomalies with Variational Autoencoders—0
Estimation of Solar Spectral Irradiance Using Meteorological Data and Analysis of Optimal Conditions for Solar Power Generation—0
ETLNet: An Efficient TCN-BiLSTM Network for Road Anomaly Detection Using Smartphone Sensors—0
A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis—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