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 2251–2275 of 4856 papers

TitleStatusHype
Surprisal Driven k-NN for Robust and Interpretable Nonparametric Learning—0
Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection—0
Weakly Supervised Anomaly Detection for Chest X-Ray ImageCode0
Approaching adverse event detection utilizing transformers on clinical time-series—0
An Improved Anomaly Detection Model for Automated Inspection of Power Line Insulators—0
Identifying Light-curve Signals with a Deep Learning Based Object Detection Algorithm. II. A General Light Curve Classification FrameworkCode0
VegaEdge: Edge AI Confluence Anomaly Detection for Real-Time Highway IoT-ApplicationsCode0
ADAMM: Anomaly Detection of Attributed Multi-graphs with Metadata: A Unified Neural Network ApproachCode0
Synchrophasor Data Anomaly Detection on Grid Edge by 5G Communication and Adjacent Compute—0
Open-Vocabulary Video Anomaly Detection—0
KnowSafe: Combined Knowledge and Data Driven Hazard Mitigation in Artificial Pancreas Systems—0
Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Data—0
DeepQC: A Deep Learning System for Automatic Quality Control of In-situ Soil Moisture Sensor Time Series Data—0
Open-Set Graph Anomaly Detection via Normal Structure Regularisation—0
CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection—0
VT-Former: An Exploratory Study on Vehicle Trajectory Prediction for Highway Surveillance through Graph Isomorphism and Transformer—0
GRAM: An Interpretable Approach for Graph Anomaly Detection using Gradient Attention Maps—0
k-Parameter Approach for False In-Season Anomaly Suppression in Daily Time Series Anomaly Detection—0
Ball Mill Fault Prediction Based on Deep Convolutional Auto-Encoding Network—0
Explained anomaly detection in text reviews: Can subjective scenarios be correctly evaluated?—0
Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter—0
A Deep Learning Approach to Video Anomaly Detection using Convolutional Autoencoders—0
Image-Pointcloud Fusion based Anomaly Detection using PD-REAL DatasetCode0
NEURO HAND: A weakly supervised Hierarchical Attention Network for interpretable neuroimaging abnormality Detection—0
Temporal Shift -- Multi-Objective Loss Function for Improved Anomaly Fall 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