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 951–975 of 4856 papers

TitleStatusHype
Abnormality Detection in Musculoskeletal Radiographs with Convolutional Neural Networks(Ensembles) and Performance Optimization—0
Causal Modeling in Multi-Context Systems: Distinguishing Multiple Context-Specific Causal Graphs which Account for Observational Support—0
Anomaly Detection for Multivariate Time Series of Exotic Supernovae—0
A GAN-based data poisoning framework against anomaly detection in vertical federated learning—0
AGAD: Adversarial Generative Anomaly Detection—0
Anomaly Detection for Industrial Big Data—0
Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review—0
Real-Time Anomaly Detection with Synthetic Anomaly Monitoring (SAM)—0
Anomaly Detection for Incident Response at Scale—0
Anomaly Detection for imbalanced datasets with Deep Generative Models—0
A Fuzzy Reinforcement LSTM-based Long-term Prediction Model for Fault Conditions in Nuclear Power Plants—0
Anomaly Detection for High-Dimensional Data Using Large Deviations Principle—0
Abnormality Detection in Mammography using Deep Convolutional Neural Networks—0
2DSig-Detect: a semi-supervised framework for anomaly detection on image data using 2D-signatures—0
A Causal Approach to Detecting Multivariate Time-series Anomalies and Root Causes—0
Anomaly Detection for Fraud in Cryptocurrency Time Series—0
AFSC: Adaptive Fourier Space Compression for Anomaly Detection—0
Automatic Anomaly Detection for Dysarthria across Two Speech Styles: Read vs Spontaneous Speech—0
Automated visual inspection of CMS HGCAL silicon sensor surface using an ensemble of a deep convolutional autoencoder and classifier—0
Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning—0
AdaFlow: Domain-Adaptive Density Estimator with Application to Anomaly Detection and Unpaired Cross-Domain Translation—0
Automated, real-time hospital ICU emergency signaling: A field-level implementation—0
Automated Real-time Anomaly Detection in Human Trajectories using Sequence to Sequence Networks—0
Automatic Bayesian Density Analysis—0
An Improved Anomaly Detection Model for Automated Inspection of Power Line Insulators—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