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 1951–1975 of 4856 papers

TitleStatusHype
Enabling Efficient Privacy-Assured Outlier Detection over Encrypted Incremental Datasets—0
Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models—0
Empirical performance maximization for linear rank statistics—0
Attire-Based Anomaly Detection in Restricted Areas Using YOLOv8 for Enhanced CCTV Security—0
Empirical Density Estimation based on Spline Quasi-Interpolation with applications to Copulas clustering modeling—0
Empirical Analysis of Anomaly Detection on Hyperspectral Imaging Using Dimension Reduction Methods—0
Attention to Patterns is all you need for Insider threat detection—0
Anomaly Detection and Sampling Cost Control via Hierarchical GANs—0
Aero-LLM: A Distributed Framework for Secure UAV Communication and Intelligent Decision-Making—0
Emotion-Based Crowd Representation for Abnormality Detection—0
EMO\&LY (EMOtion and AnomaLY) : A new corpus for anomaly detection in an audiovisual stream with emotional context.—0
Experiments on Anomaly Detection in Autonomous Driving by Forward-Backward Style Transfers—0
Expert enhanced dynamic time warping based anomaly detection—0
Explainable AI (XAI) for PHM of Industrial Asset: A State-of-The-Art, PRISMA-Compliant Systematic Review—0
Attention Modules Improve Modern Image-Level Anomaly Detection: A DifferNet Case Study—0
ELUQuant: Event-Level Uncertainty Quantification in Deep Inelastic Scattering—0
Elsa: Energy-based learning for semi-supervised anomaly detection—0
Anomaly Detection and Radio-frequency Interference Classification with Unsupervised Learning in Narrowband Radio Technosignature Searches—0
Attention Map-guided Two-stage Anomaly Detection using Hard Augmentation—0
Electrical Grid Anomaly Detection via Tensor Decomposition—0
Attention-Guided Perturbation for Unsupervised Image Anomaly Detection—0
Anomaly Detection and Removal Using Non-Stationary Gaussian Processes—0
Aero-engines Anomaly Detection using an Unsupervised Fisher Autoencoder—0
Explainable multi-class anomaly detection on functional data—0
Active Learning for Network Intrusion Detection—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