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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 2401–2450 of 4856 papers

TitleStatusHype
Unsupervised Visual Defect Detection with Score-Based Generative Model—0
G-CMP: Graph-enhanced Contextual Matrix Profile for unsupervised anomaly detection in sensor-based remote health monitoring—0
ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awareness—0
MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly DetectionCode1
A Study of Representational Properties of Unsupervised Anomaly Detection in Brain MRICode0
Lightning Fast Video Anomaly Detection via Adversarial Knowledge DistillationCode0
An Anomaly Detection Method for Satellites Using Monte Carlo Dropout—0
Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic SegmentationCode1
Composite Score for Anomaly Detection in Imbalanced Real-World Industrial Dataset—0
A Deep Learning Anomaly Detection Method in Textual Data—0
RbA: Segmenting Unknown Regions Rejected by AllCode1
MAEDAY: MAE for few and zero shot AnomalY-DetectionCode1
MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection—0
Detecting Anomalies using Generative Adversarial Networks on Images—0
Meta-Learning for Automated Selection of Anomaly Detectors for Semi-Supervised Datasets—0
Learning Invariant Rules from Data for Interpretable Anomaly DetectionCode0
Corn Yield Prediction based on Remotely Sensed Variables Using Variational Autoencoder and Multiple Instance Regression—0
CoMadOut -- A Robust Outlier Detection Algorithm based on CoMADCode0
An Adaptive Event-based Data Converter for Always-on Biomedical Applications at the Edge—0
FRE: A Fast Method For Anomaly Detection And Segmentation—0
PNI : Industrial Anomaly Detection using Position and Neighborhood InformationCode1
A Novel Center-based Deep Contrastive Metric Learning Method for the Detection of Polymicrogyria in Pediatric Brain MRICode0
Generalizable Industrial Visual Anomaly Detection with Self-Induction Vision Transformer—0
U-Flow: A U-shaped Normalizing Flow for Anomaly Detection with Unsupervised ThresholdCode1
MGADN: A Multi-task Graph Anomaly Detection Network for Multivariate Time Series—0
STGlow: A Flow-based Generative Framework with Dual Graphormer for Pedestrian Trajectory Prediction—0
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly DetectionCode1
Machine Learning Methods for Anomaly Detection in Nuclear Power Plant Power Transformers—0
Normalizing Flows for Human Pose Anomaly DetectionCode1
Pedestrian Spatio-Temporal Information Fusion For Video Anomaly Detection—0
Normal Reference Attention and Defective Feature Perception Network for Surface Defect Detection—0
Rare Yet Popular: Evidence and Implications from Labeled Datasets for Network Anomaly Detection—0
Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation—0
Dynamic Interactional And Cooperative Network For Shield Machine—0
SpectNet : End-to-End Audio Signal Classification Using Learnable SpectrogramsCode0
Heart Abnormality Detection from Heart Sound Signals using MFCC Feature and Dual Stream Attention Based Network—0
Anomaly Detection via Multi-Scale Contrasted Memory—0
On the Connection of Generative Models and Discriminative Models for Anomaly Detection—0
Are we certain it's anomalous?Code1
LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly DetectionCode0
Cyber-Attack Event Analysis for EV Charging Stations—0
Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease PredictionCode1
FAPM: Fast Adaptive Patch Memory for Real-time Industrial Anomaly DetectionCode1
Attacking Face Recognition with T-shirts: Database, Vulnerability Assessment and Detection—0
LGN-Net: Local-Global Normality Network for Video Anomaly DetectionCode1
Detecting Disengagement in Virtual Learning as an Anomaly using Temporal Convolutional Network Autoencoder—0
Far Away in the Deep Space: Dense Nearest-Neighbor-Based Out-of-Distribution DetectionCode1
A Comprehensive Survey of Transformers for Computer Vision—0
Computer Vision on X-ray Data in Industrial Production and Security Applications: A Comprehensive Survey—0
Deep Learning for Time Series Anomaly Detection: A SurveyCode1
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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