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 3301–3325 of 4856 papers

TitleStatusHype
A general anomaly detection framework for fleet-based condition monitoring of machines—0
A General Framework for Unsupervised Anomaly Detection—0
Deep Active Learning for Anomaly Detection—0
A general-purpose method for applying Explainable AI for Anomaly Detection—0
A Generic Machine Learning Framework for Fully-Unsupervised Anomaly Detection with Contaminated Data—0
A geometric framework for outlier detection in high-dimensional data—0
An Agglomerative Clustering of Simulation Output Distributions Using Regularized Wasserstein Distance—0
A Graph Encoder-Decoder Network for Unsupervised Anomaly Detection—0
A Hierarchical Approach to Conditional Random Fields for System Anomaly Detection—0
A Hierarchically Feature Reconstructed Autoencoder for Unsupervised Anomaly Detection—0
A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos—0
A Hybrid Approach for Smart Alert Generation—0
A Hybrid Convolutional Neural Network with Meta Feature Learning for Abnormality Detection in Wireless Capsule Endoscopy Images—0
A Hybrid Deep Learning Anomaly Detection Framework for Intrusion Detection—0
A hybrid IndRNNLSTM approach for real-time anomaly detection in software-defined networks—0
AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics—0
AI-based particle track identification in scintillating fibres read out with imaging sensors—0
AI-Driven Health Monitoring of Distributed Computing Architecture: Insights from XGBoost and SHAP—0
AI-Driven IRM: Transforming insider risk management with adaptive scoring and LLM-based threat detection—0
AI-Driven Multi-Stage Computer Vision System for Defect Detection in Laser-Engraved Industrial Nameplates—0
AI-Enhanced Inverter Fault and Anomaly Detection System for Distributed Energy Resources in Microgrids—0
AI for human assessment: What do professional assessors need?—0
AI Guided Early Screening of Cervical Cancer—0
AIOps-Driven Enhancement of Log Anomaly Detection in Unsupervised Scenarios—0
AI Persuasion, Bayesian Attribution, and Career Concerns of Doctors—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