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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 1001–1050 of 4856 papers

TitleStatusHype
Balancing Privacy and Action Performance: A Penalty-Driven Approach to Image Anonymization—0
Ball Mill Fault Prediction Based on Deep Convolutional Auto-Encoding Network—0
Anomaly Detection for an E-commerce Pricing System—0
A Framework of Sparse Online Learning and Its Applications—0
CoDetect: Financial Fraud Detection With Anomaly Feature Detection—0
Battery Cloud with Advanced Algorithms—0
Battery State of Health Estimation Using LLM Framework—0
Bayesian Anomaly Detection and Classification—0
Automated Antenna Testing Using Encoder-Decoder-based Anomaly Detection—0
Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection—0
Bayesian generative models can flag performance loss, bias, and out-of-distribution image content—0
Bayesian Hypernetworks—0
Bayesian Learning of Clique Tree Structure—0
Anomaly Detection for Water Treatment System based on Neural Network with Automatic Architecture Optimization—0
Automated Anomaly Detection on European XFEL Klystrons—0
Anomaly Detection for Aggregated Data Using Multi-Graph Autoencoder—0
Bayesian Time Series Forecasting with Change Point and Anomaly Detection—0
Anomaly Detection Dataset for Industrial Control Systems—0
Behavioral Anomaly Detection in Distributed Systems via Federated Contrastive Learning—0
A geometric framework for outlier detection in high-dimensional data—0
Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors—0
Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics—0
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types—0
A Framework for Verifiable and Auditable Federated Anomaly Detection—0
Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks—0
Autoencoding Features for Aviation Machine Learning Problems—0
Anomaly Detection by Robust Statistics—0
Autoencoding Binary Classifiers for Supervised Anomaly Detection—0
Autoencoders for unsupervised anomaly detection in high energy physics—0
A Framework for End-to-End Deep Learning-Based Anomaly Detection in Transportation Networks—0
CNC: Cross-modal Normality Constraint for Unsupervised Multi-class Anomaly Detection—0
Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network—0
Comparative Study on Supervised versus Semi-supervised Machine Learning for Anomaly Detection of In-vehicle CAN Network—0
Autoencoders for Semivisible Jet Detection—0
Autoencoders for Real-Time SUEP Detection—0
Autoencoders for Anomaly Detection are Unreliable—0
Anomaly Detection by One Class Latent Regularized Networks—0
A framework for anomaly detection using language modeling, and its applications to finance—0
Anomaly Detection by Context Contrasting—0
AutoEncoder Convolutional Neural Network for Pneumonia Detection—0
Anomaly Detection By Autoencoder Based On Weighted Frequency Domain Loss—0
Autoencoder-based Online Data Quality Monitoring for the CMS Electromagnetic Calorimeter—0
Autoencoder-Based Detection of Anomalous Stokes V Spectra in the Flare-Producing Active Region 13663 Using Hinode/SP Observations—0
Activity report analysis with automatic single or multispan answer extraction—0
Clustering-based Anomaly Detection for microservices—0
Autoencoder-based Condition Monitoring and Anomaly Detection Method for Rotating Machines—0
Autoencoder-based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter—0
Anomaly Detection by Adapting a pre-trained Vision Language Model—0
Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptation—0
Autoencoder based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems—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