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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 2251–2300 of 4856 papers

TitleStatusHype
Complying with the EU AI Act: Innovations in Explainable and User-Centric Hand Gesture Recognition—0
ComplexVAD: Detecting Interaction Anomalies in Video—0
Anomaly Detection on X-Rays Using Self-Supervised Aggregation Learning—0
Competing Topic Naming Conventions in Quora: Predicting Appropriate Topic Merges and Winning Topics from Millions of Topic Pairs—0
AMAE: Adaptation of Pre-Trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays—0
AMAD: AutoMasked Attention for Unsupervised Multivariate Time Series Anomaly Detection—0
Comparison of RNN Encoder-Decoder Models for Anomaly Detection—0
Comparison of Optimizers for Fault Isolation and Diagnostics of Control Rod Drives—0
Anomaly Detection on IT Operation Series via Online Matrix Profile—0
Comparison of Evolving Granular Classifiers applied to Anomaly Detection for Predictive Maintenance in Computing Centers—0
Anomaly Detection on Graph Time Series—0
Unsupervised in-distribution anomaly detection of new physics through conditional density estimation—0
Holistic Features For Real-Time Crowd Behaviour Anomaly Detection—0
Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection—0
Comparative Study on Supervised versus Semi-supervised Machine Learning for Anomaly Detection of In-vehicle CAN Network—0
AMAD: Adversarial Multiscale Anomaly Detection on High-Dimensional and Time-Evolving Categorical Data—0
Comparative Study on Semi-supervised Learning Applied for Anomaly Detection in Hydraulic Condition Monitoring System—0
A Dataset Fusion Algorithm for Generalised Anomaly Detection in Homogeneous Periodic Time Series Datasets—0
3D Reasoning for Unsupervised Anomaly Detection in Pediatric WbMRI—0
Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Payments—0
Community-Level Anomaly Detection for Anti-Money Laundering—0
Community-based anomaly detection using spectral graph filtering—0
Combining Switching Mechanism with Re-Initialization and Anomaly Detection for Resiliency of Cyber-Physical Systems—0
Anomaly Detection of UAV State Data Based on Single-class Triangular Global Alignment Kernel Extreme Learning Machine—0
A Machine-Synesthetic Approach To DDoS Network Attack Detection—0
Hoi2Anomaly: An Explainable Anomaly Detection Approach Guided by Human-Object Interaction—0
How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?—0
Human-Free Automated Prompting for Vision-Language Anomaly Detection: Prompt Optimization with Meta-guiding Prompt Scheme—0
Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder—0
Configurable Independent Component Analysis Preprocessing Accelerator—0
A Machine-Learning Phase Classification Scheme for Anomaly Detection in Signals with Periodic Characteristics—0
Collective Awareness for Abnormality Detection in Connected Autonomous Vehicles—0
A Dataset for Semantic Segmentation in the Presence of Unknowns—0
Hi-SAM: A high-scalable authentication model for satellite-ground Zero-Trust system using mean field game—0
Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network—0
Anomaly Detection of Tabular Data Using LLMs—0
Collaborative Anomaly Detection—0
Anomaly Detection of Smart Metering System for Power Management with Battery Storage System/Electric Vehicle—0
A Machine Learning-based Framework for Predictive Maintenance of Semiconductor Laser for Optical Communication—0
Histogram- and Diffusion-Based Medical Out-of-Distribution Detection—0
Coincident Learning for Unsupervised Anomaly Detection—0
COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection—0
Anomaly Detection of Particle Orbit in Accelerator using LSTM Deep Learning Technology—0
CoDetect: Financial Fraud Detection With Anomaly Feature Detection—0
A Machine Learning-based Anomaly Detection Framework in Life Insurance Contracts—0
Anomaly Detection of Command Shell Sessions based on DistilBERT: Unsupervised and Supervised Approaches—0
CNC: Cross-modal Normality Constraint for Unsupervised Multi-class Anomaly Detection—0
Operational range bounding of spectroscopy models with anomaly detection—0
Anomaly Detection Models for IoT Time Series Data—0
ClusterLog: Clustering Logs for Effective Log-based Anomaly 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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (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