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 2276–2300 of 4856 papers

TitleStatusHype
Hoi2Anomaly: An Explainable Anomaly Detection Approach Guided by Human-Object Interaction—0
How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?—0
Human-Free Automated Prompting for Vision-Language Anomaly Detection: Prompt Optimization with Meta-guiding Prompt Scheme—0
Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder—0
Configurable Independent Component Analysis Preprocessing Accelerator—0
A Machine-Learning Phase Classification Scheme for Anomaly Detection in Signals with Periodic Characteristics—0
Collective Awareness for Abnormality Detection in Connected Autonomous Vehicles—0
A Dataset for Semantic Segmentation in the Presence of Unknowns—0
Hi-SAM: A high-scalable authentication model for satellite-ground Zero-Trust system using mean field game—0
Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network—0
Anomaly Detection of Tabular Data Using LLMs—0
Collaborative Anomaly Detection—0
Anomaly Detection of Smart Metering System for Power Management with Battery Storage System/Electric Vehicle—0
A Machine Learning-based Framework for Predictive Maintenance of Semiconductor Laser for Optical Communication—0
Histogram- and Diffusion-Based Medical Out-of-Distribution Detection—0
Coincident Learning for Unsupervised Anomaly Detection—0
COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection—0
Anomaly Detection of Particle Orbit in Accelerator using LSTM Deep Learning Technology—0
CoDetect: Financial Fraud Detection With Anomaly Feature Detection—0
A Machine Learning-based Anomaly Detection Framework in Life Insurance Contracts—0
Anomaly Detection of Command Shell Sessions based on DistilBERT: Unsupervised and Supervised Approaches—0
CNC: Cross-modal Normality Constraint for Unsupervised Multi-class Anomaly Detection—0
Operational range bounding of spectroscopy models with anomaly detection—0
Anomaly Detection Models for IoT Time Series Data—0
ClusterLog: Clustering Logs for Effective Log-based Anomaly Detection—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