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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2501–2525 of 4856 papers

TitleStatusHype
CSCLog: A Component Subsequence Correlation-Aware Log Anomaly Detection MethodCode0
Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization—0
Contextual Affinity Distillation for Image Anomaly Detection—0
That's BAD: Blind Anomaly Detection by Implicit Local Feature Clustering—0
TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformersCode0
Data-driven Predictive Latency for 5G: A Theoretical and Experimental Analysis Using Network Measurements—0
Anomaly detection in image or latent space of patch-based auto-encoders for industrial image analysis—0
Prototypes as Explanation for Time Series Anomaly Detection—0
The ROAD to discovery: machine learning-driven anomaly detection in radio astronomy spectrogramsCode0
Application of MUSIC-type imaging for anomaly detection without background information—0
Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images—0
Morse Neural Networks for Uncertainty Quantification—0
A MIL Approach for Anomaly Detection in Surveillance Videos from Multiple Camera ViewsCode0
Hiding in Plain Sight: Differential Privacy Noise Exploitation for Evasion-resilient Localized Poisoning Attacks in Multiagent Reinforcement Learning—0
Applied Bayesian Structural Health Monitoring: inclinometer data anomaly detection and forecasting—0
A Comparative Study of Machine Learning Algorithms for Anomaly Detection in Industrial Environments: Performance and Environmental Impact—0
Interpretable Anomaly Detection in Cellular Networks by Learning Concepts in Variational Autoencoders—0
Exploring Dual Model Knowledge Distillation for Anomaly Detection—0
Learning normal asymmetry representations for homologous brain structuresCode0
Anomaly Detection in Networks via Score-Based Generative ModelsCode0
Autoencoders for Real-Time SUEP Detection—0
OptIForest: Optimal Isolation Forest for Anomaly DetectionCode0
Triggering Dark Showers with Conditional Dual Auto-EncodersCode0
Targeted collapse regularized autoencoder for anomaly detection: black hole at the center—0
End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection—0
Show:102550
← PrevPage 101 of 195Next →
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