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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 2901–2950 of 4856 papers

TitleStatusHype
Battery Cloud with Advanced Algorithms—0
Object-centric and memory-guided normality reconstruction for video anomaly detection—0
Machine Learning based Anomaly Detection for Smart Shirt: A Systematic Review—0
Coresets for Data Discretization and Sine Wave Fitting—0
Hybrid Deep Learning Model using SPCAGAN Augmentation for Insider Threat Analysis—0
Flurry: a Fast Framework for Reproducible Multi-layered Provenance Graph Representation Learning—0
Exploring Scalable, Distributed Real-Time Anomaly Detection for Bridge Health MonitoringCode0
The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods—0
Abuse and Fraud Detection in Streaming Services Using Heuristic-Aware Machine Learning—0
Data-Efficient and Interpretable Tabular Anomaly Detection—0
Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With SupervoxelsCode1
Learning Neural Set Functions Under the Optimal Subset OracleCode1
Anomaly Detection in Big Data—0
Unsupervised Anomaly Detection from Time-of-Flight Depth Images—0
MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasksCode1
Efficient Dynamic Clustering: Capturing Patterns from Historical Cluster Evolution—0
Addressing Gap between Training Data and Deployed Environment by On-Device LearningCode0
Omni-frequency Channel-selection Representations for Unsupervised Anomaly DetectionCode1
Anomaly Detection in File Fragment Classification of Image File Formats—0
Distributed-MPC with Data-Driven Estimation of Bus Admittance Matrix in Voltage Control—0
Domain Knowledge-Informed Self-Supervised Representations for Workout Form AssessmentCode1
Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection—0
Data refinement for fully unsupervised visual inspection using pre-trained networks—0
Self-Supervised and Interpretable Anomaly Detection using Network Transformers—0
Do autoencoders need a bottleneck for anomaly detection?—0
Stacked Residuals of Dynamic Layers for Time Series Anomaly Detection—0
Statistics and Deep Learning-based Hybrid Model for Interpretable Anomaly Detection—0
Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations—0
A spectral-spatial fusion anomaly detection method for hyperspectral imagery—0
Augmentation based unsupervised domain adaptation—0
Deep Graph Learning for Anomalous Citation Detection—0
ML-based Anomaly Detection in Optical Fiber Monitoring—0
Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors—0
Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization—0
ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variables—0
Recurrent Auto-Encoder With Multi-Resolution Ensemble and Predictive Coding for Multivariate Time-Series Anomaly Detection—0
ICSML: Industrial Control Systems ML Framework for native inference using IEC 61131-3 codeCode1
Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants—0
A Novel Anomaly Detection Method for Multimodal WSN Data Flow via a Dynamic Graph Neural Network—0
Anomalib: A Deep Learning Library for Anomaly Detection—0
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time SeriesCode3
Latent Outlier Exposure for Anomaly Detection with Contaminated DataCode1
Federated-Learning-Based Anomaly Detection for IoT Security Attacks—0
Trustworthy Anomaly Detection: A Survey—0
Simulating Malicious Attacks on VANETs for Connected and Autonomous Vehicle Cybersecurity: A Machine Learning Dataset—0
Transformers in Time Series: A SurveyCode4
Deep Learning-based Anomaly Detection on X-ray Images of Fuel Cell Electrodes—0
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly DetectionCode1
A Survey of Visual Sensory Anomaly DetectionCode1
DeCorus: Hierarchical Multivariate Anomaly Detection at Cloud-Scale—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