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 3076–3100 of 4856 papers

TitleStatusHype
Early Abnormal Detection of Sewage Pipe Network: Bagging of Various Abnormal Detection Algorithms—0
Perturbation Learning Based Anomaly Detection—0
A Three-Stage Anomaly Detection Framework for Traffic VideosCode0
CAINNFlow: Convolutional block Attention modules and Invertible Neural Networks Flow for anomaly detection and localization tasks—0
Invertible Neural Networks for Graph PredictionCode0
Dual-stream spatiotemporal networks with feature sharing for monitoring animals in the home cage—0
Proximally Sensitive Error for Anomaly Detection and Feature Learning—0
Attack-Agnostic Adversarial Detection—0
Détection d’anomalies textuelles à base de l’ingénierie d’invite (Prompt Engineering-Based Text Anomaly Detection )—0
‘Am I the Bad One’? Predicting the Moral Judgement of the Crowd Using Pre–trained Language Models—0
Robust Projection based Anomaly Extraction (RPE) in Univariate Time-Series—0
MAD-EN: Microarchitectural Attack Detection through System-wide Energy ConsumptionCode0
Benchmarking Unsupervised Anomaly Detection and Localization—0
Grid HTM: Hierarchical Temporal Memory for Anomaly Detection in VideosCode0
Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection—0
Unfooling Perturbation-Based Post Hoc ExplainersCode0
Ensemble2: Anomaly Detection via EVT-Ensemble Framework for Seasonal KPIs in Communication Network—0
FadMan: Federated Anomaly Detection across Multiple Attributed Networks—0
PSL is Dead. Long Live PSL—0
Towards Symbolic Time Series Representation Improved by Kernel Density Estimators—0
Neural Contextual Bandits Based Dynamic Sensor Selection for Low-Power Body-Area Networks—0
Naive Few-Shot Learning: Uncovering the fluid intelligence of machines—0
Faithful Explanations for Deep Graph Models—0
Psychotic Relapse Prediction in Schizophrenia Patients using A Mobile Sensing-based Supervised Deep Learning Model—0
GraphAD: A Graph Neural Network for Entity-Wise Multivariate Time-Series Anomaly Detection—0
Show:102550
← PrevPage 124 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