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 3526–3550 of 4856 papers

TitleStatusHype
Anomaly Detection in Clutter using Spectrally Enhanced Ladar—0
Anomaly detection in Context-aware Feature Models—0
Anomaly detection in cross-country money transfer temporal networks—0
Cybersecurity Anomaly Detection in Adversarial Environments—0
Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach—0
Anomaly Detection in Driving by Cluster Analysis Twice—0
Anomaly detection in dynamical systems from measured time series—0
Anomaly Detection in Dynamic Graphs: A Comprehensive Survey—0
Anomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis Through Self-Supervised Learning—0
Anomaly Detection in File Fragment Classification of Image File Formats—0
Anomaly Detection in Global Financial Markets with Graph Neural Networks and Nonextensive Entropy—0
Anomaly Detection in Graph Structured Data: A Survey—0
Anomaly Detection in Hierarchical Data Streams under Unknown Models—0
Anomaly Detection in High Performance Computers: A Vicinity Perspective—0
Anomaly Detection in Image Datasets Using Convolutional Neural Networks, Center Loss, and Mahalanobis Distance—0
Anomaly detection in image or latent space of patch-based auto-encoders for industrial image analysis—0
Anomaly Detection in Industrial Machinery using IoT Devices and Machine Learning: a Systematic Mapping—0
Anomaly Detection in Intra-Vehicle Networks—0
Anomaly Detection in Large Labeled Multi-Graph Databases—0
Anomaly Detection in Large Scale Networks with Latent Space Models—0
Anomaly detection in laser-guided vehicles' batteries: a case study—0
Anomaly Detection in Medical Imaging -- A Mini Review—0
Anomaly detection in non-stationary videos using time-recursive differencing network based prediction—0
Anomaly Detection in Offshore Wind Turbine Structures using Hierarchical Bayesian Modelling—0
Anomaly Detection in OKTA Logs using Autoencoders—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