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 1626–1650 of 4856 papers

TitleStatusHype
Detection of Anomalies in Multivariate Time Series Using Ensemble Techniques—0
Detection of Dataset Shifts in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression—0
Detection of Emerging Infectious Diseases in Lung CT based on Spatial Anomaly Patterns—0
Detection of Fights in Videos: A Comparison Study of Anomaly Detection and Action Recognition—0
Detection of fraudulent financial papers by picking a collection of characteristics using optimization algorithms and classification techniques based on squirrels—0
Detection of Global Anomalies on Distributed IoT Edges with Device-to-Device Communication—0
Detection of Object Throwing Behavior in Surveillance Videos—0
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications—0
Detection of Shilling Attack Based on T-distribution on the Dynamic Time Intervals in Recommendation Systems—0
Detection of Thin Boundaries between Different Types of Anomalies in Outlier Detection using Enhanced Neural Networks—0
Detection of Unknown Anomalies in Streaming Videos with Generative Energy-based Boltzmann Models—0
A Robust and Efficient Multi-Scale Seasonal-Trend Decomposition—0
Determinação Automática de Limiar de Detecção de Ataques em Redes de Computadores Utilizando Autoencoders—0
Develop End-to-End Anomaly Detection System—0
Devil in the Detail: Attack Scenarios in Industrial Applications—0
DFM: Differentiable Feature Matching for Anomaly Detection—0
DFM: Interpolant-free Dual Flow Matching—0
Efficient Anomaly Detection via Matrix Sketching—0
DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions—0
Anomaly Subsequence Detection with Dynamic Local Density for Time Series—0
Diagnosis driven Anomaly Detection for CPS—0
An Anomaly Detection System Based on Generative Classifiers for Controller Area Network—0
Diagnostics Using Nuclear Plant Cyber Attack Analysis Toolkit—0
Dictionary learning approach to monitoring of wind turbine drivetrain bearings—0
An Anomaly Detection Method for Satellites Using Monte Carlo Dropout—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