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 3501–3525 of 4856 papers

TitleStatusHype
Anomaly Detection for People with Visual Impairments Using an Egocentric 360-Degree Camera—0
Anomaly Detection for Real-World Cyber-Physical Security using Quantum Hybrid Support Vector Machines—0
Anomaly Detection for Scalable Task Grouping in Reinforcement Learning-based RAN Optimization—0
Anomaly Detection for Skin Disease Images Using Variational Autoencoder—0
Anomaly Detection for Tabular Data with Internal Contrastive Learning—0
Anomaly detection for the identification of volcanic unrest in satellite imagery—0
Anomaly Detection for Unmanned Aerial Vehicle Sensor Data Using a Stacked Recurrent Autoencoder Method with Dynamic Thresholding—0
Anomaly Detection for Water Treatment System based on Neural Network with Automatic Architecture Optimization—0
Anomaly Detection Framework Using Rule Extraction for Efficient Intrusion Detection—0
Anomaly Detection from a Tensor Train Perspective—0
Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors—0
Anomaly Detection in a Digital Video Broadcasting System Using Timed Automata—0
Anomaly Detection in Aeronautics Data with Quantum-compatible Discrete Deep Generative Model—0
Anomaly Detection in a Large-scale Cloud Platform—0
Anomaly Detection in Automated Fibre Placement: Learning with Data Limitations—0
Anomaly Detection in Automatic Generation Control Systems Based on Traffic Pattern Analysis and Deep Transfer Learning—0
Interpretable Machine Learning Models for Predicting and Explaining Vehicle Fuel Consumption Anomalies—0
Anomaly Detection in Beehives: An Algorithm Comparison—0
Anomaly Detection in Beehives using Deep Recurrent Autoencoders—0
Anomaly Detection in Big Data—0
Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methods—0
Anomaly Detection in California Electricity Price Forecasting: Enhancing Accuracy and Reliability Using Principal Component Analysis—0
Anomaly Detection in Certificate Transparency Logs—0
A versatile anomaly detection method for medical images with a flow-based generative model in semi-supervision setting—0
Anomaly Detection in Cloud Components—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