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 3976–4000 of 4856 papers

TitleStatusHype
Partial AUC Maximization via Nonlinear Scoring Functions—0
PatchFlow: Leveraging a Flow-Based Model with Patch Features—0
Patch Spatio-Temporal Relation Prediction for Video Anomaly Detection—0
PatchTrAD: A Patch-Based Transformer focusing on Patch-Wise Reconstruction Error for Time Series Anomaly Detection—0
Patch vs. Global Image-Based Unsupervised Anomaly Detection in MR Brain Scans of Early Parkinsonian Patients—0
Patch-wise Auto-Encoder for Visual Anomaly Detection—0
Patchwise Sparse Dictionary Learning from pre-trained Neural Network Activation Maps for Anomaly Detection in Images—0
Pattern-Based Time-Series Risk Scoring for Anomaly Detection and Alert Filtering -- A Predictive Maintenance Case Study—0
PEDENet: Image Anomaly Localization via Patch Embedding and Density Estimation—0
Pedestrian Spatio-Temporal Information Fusion For Video Anomaly Detection—0
Pediatric Otoscopy Video Screening with Shift Contrastive Anomaly Detection—0
Peek Inside the Closed World: Evaluating Autoencoder-Based Detection of DDoS to Cloud—0
Perfect density models cannot guarantee anomaly detection—0
Performance Analysis of a Foreground Segmentation Neural Network Model—0
Performance Comparison and Implementation of Bayesian Variants for Network Intrusion Detection—0
Performance Examination of Symbolic Aggregate Approximation in IoT Applications—0
Permutation invariant Gaussian matrix models for financial correlation matrices—0
Persistent Homology for Breast Tumor Classification using Mammogram Scans—0
Personalized Anomaly Detection in PPG Data using Representation Learning and Biometric Identification—0
Personalized Early Stage Alzheimer's Disease Detection: A Case Study of President Reagan's Speeches—0
Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data—0
Perturbation Learning Based Anomaly Detection—0
Pets: General Pattern Assisted Architecture For Time Series Analysis—0
PhoGAD: Graph-based Anomaly Behavior Detection with Persistent Homology Optimization—0
Unsupervised Dual Adversarial Learning for Anomaly Detection in Colonoscopy Video Frames—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