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 4826–4850 of 4856 papers

TitleStatusHype
Network Traffic Decomposition for Anomaly Detection—0
Sleep Analytics and Online Selective Anomaly Detection—0
Classification Tree Diagrams in Health Informatics Applications—0
Toward Supervised Anomaly Detection—0
Anomaly detection in reconstructed quantum states using a machine-learning technique—0
Intrusion Detection using Continuous Time Bayesian Networks—0
Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic—0
Scalable Anomaly Detection in Large Homogenous Populations—0
Network Anomaly Detection: A Survey and Comparative Analysis of Stochastic and Deterministic Methods—0
Capturing Anomalies in the Choice of Content Words in Compositional Distributional Semantic Space—0
A Review of Machine Learning based Anomaly Detection Techniques—0
Quiet in Class: Classification, Noise and the Dendritic Cell Algorithm—0
Anomaly Detection via oversampling Principal Component AnalysisCode0
Syntactic sensitive complexity for symbol-free sequence—0
Narrative based Postdictive Reasoning for Cognitive Robotics—0
Object-Centric Anomaly Detection by Attribute-Based Reasoning—0
Improved Anomaly Detection in Crowded Scenes via Cell-based Analysis of Foreground Speed, Size and Texture—0
A Massively Parallel Associative Memory Based on Sparse Neural Networks—0
One-Class Support Measure Machines for Group Anomaly Detection—0
Multi-criteria Anomaly Detection using Pareto Depth Analysis—0
Securing Your Transactions: Detecting Anomalous Patterns In XML Documents—0
Kernels on Sample Sets via Nonparametric Divergence Estimates—0
Group Anomaly Detection using Flexible Genre Models—0
Efficient anomaly detection using bipartite k-NN graphs—0
Detection and Analysis of Drive-by-Download Attacks and Malicious JavaScript Code—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