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 4276–4300 of 4856 papers

TitleStatusHype
Dictionary learning approach to monitoring of wind turbine drivetrain bearings—0
Did You Hear That? Introducing AADG: A Framework for Generating Benchmark Data in Audio Anomaly Detection—0
Differential Anomaly Detection for Facial Images—0
Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation—0
Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability—0
DiffFake: Exposing Deepfakes using Differential Anomaly Detection—0
Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound—0
Digraphwave: Scalable Extraction of Structural Node Embeddings via Diffusion on Directed Graphs—0
Dimensionality Increment of PMU Data for Anomaly Detection in Low Observability Power Systems—0
Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder—0
Dimensionality reduction techniques to support insider trading detection—0
Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection—0
Directional anomaly detection—0
Disaster Anomaly Detector via Deeper FCDDs for Explainable Initial Responses—0
Discovering Imperfectly Observable Adversarial Actions using Anomaly Detection—0
Discrepancy-based Diffusion Models for Lesion Detection in Brain MRI—0
Discrete neural representations for explainable anomaly detection—0
Discriminative Deep Random Walk for Network Classification—0
Discriminative Feature Learning Framework with Gradient Preference for Anomaly Detection—0
Discriminative-Generative Dual Memory Video Anomaly Detection—0
Discriminative-Generative Representation Learning for One-Class Anomaly Detection—0
Discussion of Features for Acoustic Anomaly Detection under Industrial Disturbing Noise in an End-of-Line Test of Geared Motors—0
Disentangling Physical Parameters for Anomalous Sound Detection Under Domain Shifts—0
Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection—0
Distance-Based Anomaly Detection for Industrial Surfaces Using Triplet Networks—0
Show:102550
← PrevPage 172 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