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 2351–2375 of 4856 papers

TitleStatusHype
Graph Pre-Training Models Are Strong Anomaly Detectors—0
Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis—0
Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection—0
Classification Tree Diagrams in Health Informatics Applications—0
Classification of Anomalies in Telecommunication Network KPI Time Series—0
CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection—0
Grid Monitoring with Synchro-Waveform and AI Foundation Model Technologies—0
CL-CaGAN: Capsule differential adversarial continuous learning for cross-domain hyperspectral anomaly detection—0
Group Anomaly Detection using Flexible Genre Models—0
Grouped Convolutional Neural Networks for Multivariate Time Series—0
Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection—0
Guarding the Grid: Enhancing Resilience in Automated Residential Demand Response Against False Data Injection Attacks—0
Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art—0
Identification of temporal transition of functional states using recurrent neural networks from functional MRI—0
Client-Specific Anomaly Detection for Face Presentation Attack Detection—0
Accurate and fast anomaly detection in industrial processes and IoT environments—0
Graph neural network-based lithium-ion battery state of health estimation using partial discharging curve—0
Hankel-structured Tensor Robust PCA for Multivariate Traffic Time Series Anomaly Detection—0
CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection—0
Graph Mixture of Experts and Memory-augmented Routers for Multivariate Time Series Anomaly Detection—0
Hardware Architecture Proposal for TEDA algorithm to Data Streaming Anomaly Detection—0
Anomaly Detection in Video Sequence With Appearance-Motion Correspondence—0
Anomaly Detection in Trajectory Data with Normalizing Flows—0
Harnessing Large Language Models for Training-free Video Anomaly Detection—0
Anomaly Detection in Traffic Scenes via Spatial-aware Motion Reconstruction—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