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 3751–3775 of 4856 papers

TitleStatusHype
MissionGNN: Hierarchical Multimodal GNN-based Weakly Supervised Video Anomaly Recognition with Mission-Specific Knowledge Graph Generation—0
mixed attention auto encoder for multi-class industrial anomaly detection—0
Mixture Proportion Estimation via Kernel Embedding of Distributions—0
MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network—0
MLAD: A Unified Model for Multi-system Log Anomaly Detection—0
ML-based Anomaly Detection in Optical Fiber Monitoring—0
MLOps with Microservices: A Case Study on the Maritime Domain—0
Model-Based Monitoring and State Estimation for Digital Twins: The Kalman Filter—0
Model-driven Engineering for Machine Learning Components: A Systematic Literature Review—0
Model Evaluation and Anomaly Detection in Temporal Complex Networks using Deep Learning Methods—0
Model-Free Unsupervised Anomaly Detection Framework in Multivariate Time-Series of Industrial Dynamical Systems—0
A Taxonomy and Review of Algorithms for Modeling and Predicting Human Driver Behavior—0
Modeling asset allocation strategies and a new portfolio performance score—0
Modeling Newswire Events using Neural Networks for Anomaly Detection—0
Modeling Representation of Videos for Anomaly Detection using Deep Learning: A Review—0
Model Selection for Anomaly Detection—0
Model Selection of Anomaly Detectors in the Absence of Labeled Validation Data—0
MoniLog: An Automated Log-Based Anomaly Detection System for Cloud Computing Infrastructures—0
Temporal Detection of Anomalies via Actor-Critic Based Controlled Sensing—0
Morse Neural Networks for Uncertainty Quantification—0
Motif-Consistent Counterfactuals with Adversarial Refinement for Graph-Level Anomaly Detection—0
MotifDisco: Motif Causal Discovery For Time Series Motifs—0
Motion-Aware Feature for Improved Video Anomaly Detection—0
movie2trailer: Unsupervised trailer generation using Anomaly detection—0
Moving Metric Detection and Alerting System at eBay—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