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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1751–1775 of 4856 papers

TitleStatusHype
Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection—0
Bayesian Anomaly Detection Using Extreme Value Theory—0
Bayesian Anomaly Detection and Classification—0
Battery State of Health Estimation Using LLM Framework—0
Anomaly detection for the identification of volcanic unrest in satellite imagery—0
Deep Active Learning for Anomaly Detection—0
Adaptable and Interpretable Framework for Novelty Detection in Real-Time IoT Systems—0
Abnormal Object Recognition: A Comprehensive Study—0
Battery Cloud with Advanced Algorithms—0
Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Sounds—0
Anomaly Detection for Tabular Data with Internal Contrastive Learning—0
Ball Mill Fault Prediction Based on Deep Convolutional Auto-Encoding Network—0
Balancing Privacy and Action Performance: A Penalty-Driven Approach to Image Anonymization—0
Anomaly Detection for Skin Disease Images Using Variational Autoencoder—0
Bagged Regularized k-Distances for Anomaly Detection—0
BadSAD: Clean-Label Backdoor Attacks against Deep Semi-Supervised Anomaly Detection—0
Anomaly Detection for Scalable Task Grouping in Reinforcement Learning-based RAN Optimization—0
A General Framework for Unsupervised Anomaly Detection—0
A Deep Learning Approach to Anomaly Sequence Detection for High-Resolution Monitoring of Power Systems—0
Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies with an Integrated Statistical and Neural Framework—0
Anomaly Detection for Real-World Cyber-Physical Security using Quantum Hybrid Support Vector Machines—0
Back Home: A Machine Learning Approach to Seashell Classification and Ecosystem Restoration—0
Anomaly Detection for People with Visual Impairments Using an Egocentric 360-Degree Camera—0
A general anomaly detection framework for fleet-based condition monitoring of machines—0
Background subtraction on depth videos with convolutional neural networks—0
Show:102550
← PrevPage 71 of 195Next →
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