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 4526–4550 of 4856 papers

TitleStatusHype
FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction—0
FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection—0
Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models—0
Fast Wireless Sensor Anomaly Detection based on Data Stream in Edge Computing Enabled Smart Greenhouse—0
Fault Detection in Mobile Networks Using Diffusion Models—0
Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks—0
Fault Detection Method for Power Conversion Circuits Using Thermal Image and Convolutional Autoencoder—0
Fault Detection Using Nonlinear Low-Dimensional Representation of Sensor Data—0
Fault-Diagnosing SLAM for Varying Scale Change Detection—0
Fault Diagnosis in New Wind Turbines using Knowledge from Existing Turbines by Generative Domain Adaptation—0
Fault injection analysis of Real NVP normalising flow model for satellite anomaly detection—0
FCDD - Explainable Anomaly Detection—0
Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images—0
Feature anomaly detection system (FADS) for intelligent manufacturing—0
Feature Extraction of ECG Signal Using HHT Algorithm—0
Feature Prediction Diffusion Model for Video Anomaly Detection—0
Feature Purified Transformer With Cross-level Feature Guiding Decoder For Multi-class OOD and Anomaly Deteciton—0
Feature Selection for Fault Detection and Prediction based on Event Log Analysis—0
FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly Detection in Tabular Data—0
FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation—0
FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detection—0
Federated Anomaly Detection over Distributed Data Streams—0
Federated Isolation Forest for Efficient Anomaly Detection on Edge IoT Systems—0
Federated Koopman-Reservoir Learning for Large-Scale Multivariate Time-Series Anomaly Detection—0
Federated-Learning-Based Anomaly Detection for IoT Security Attacks—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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (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