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 2851–2875 of 4856 papers

TitleStatusHype
Quantum Normalizing Flows for Anomaly Detection—0
Quantum Support Vector Regression for Robust Anomaly Detection—0
Query-based Industrial Analytics over Knowledge Graphs with Ontology Reshaping—0
Quickest Anomaly Detection in Sensor Networks With Unlabeled Samples—0
Quiet in Class: Classification, Noise and the Dendritic Cell Algorithm—0
RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration—0
RadArnomaly: Protecting Radar Systems from Data Manipulation Attacks—0
RADAR: Robust Two-stage Modality-incomplete Industrial Anomaly Detection—0
RADE: Resource-Efficient Supervised Anomaly Detection Using Decision Tree-Based Ensemble Methods—0
Radial Autoencoders for Enhanced Anomaly Detection—0
RAD: On-line Anomaly Detection for Highly Unreliable Data—0
A Radon-Nikodým Perspective on Anomaly Detection: Theory and Implications—0
Railway Anomaly detection model using synthetic defect images generated by CycleGAN—0
RandomSEMO: Normality Learning Of Moving Objects For Video Anomaly Detection—0
Random Subspace Mixture Models for Interpretable Anomaly Detection—0
Random Word Data Augmentation with CLIP for Zero-Shot Anomaly Detection—0
RAPID: Robust APT Detection and Investigation Using Context-Aware Deep Learning—0
Rare Yet Popular: Evidence and Implications from Labeled Datasets for Network Anomaly Detection—0
RATE-DISTORTION OPTIMIZATION GUIDED AUTOENCODER FOR GENERATIVE APPROACH—0
Rate-Distortion Optimization Guided Autoencoder for Isometric Embedding in Euclidean Latent Space—0
Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection—0
Real-Time Anomalous Behavior Detection and Localization in Crowded Scenes—0
Real-time Anomaly Detection and Classification in Streaming PMU Data—0
Real-Time Anomaly Detection and Localization in Crowded Scenes—0
Real-Time Anomaly Detection and Reactive Planning with Large Language Models—0
Show:102550
← PrevPage 115 of 195Next →
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