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 2476–2500 of 4856 papers

TitleStatusHype
A Review of Computer Vision Methods in Network Security—0
A Review of Machine Learning based Anomaly Detection Techniques—0
A review of neural network algorithms and their applications in supercritical extraction—0
A Review of Open Source Software Tools for Time Series Analysis—0
A Review of Physics-Informed Machine Learning Methods with Applications to Condition Monitoring and Anomaly Detection—0
A review on outlier/anomaly detection in time series data—0
Are vision language models robust to uncertain inputs?—0
Argos: Agentic Time-Series Anomaly Detection with Autonomous Rule Generation via Large Language Models—0
Identifying Backdoor Attacks in Federated Learning via Anomaly Detection—0
A Roadmap Towards Resilient Internet of Things for Cyber-Physical Systems—0
A Robust and Efficient Multi-Scale Seasonal-Trend Decomposition—0
A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics—0
A Robust Autoencoder Ensemble-Based Approach for Anomaly Detection in Text—0
A Robust Likelihood Model for Novelty Detection—0
Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed Anomaly Detection via Cache Lookups—0
Artificial Intelligence and Machine Learning in 5G Network Security: Opportunities, advantages, and future research trends—0
Artificial intelligence for abnormality detection in high volume neuroimaging: a systematic review and meta-analysis—0
A Scalable Algorithm for Anomaly Detection via Learning-Based Controlled Sensing—0
A Scalable and Generalized Deep Learning Framework for Anomaly Detection in Surveillance Videos—0
A Scalable Approach for Outlier Detection in Edge Streams Using Sketch-based Approximations—0
A scalable framework for annotating photovoltaic cell defects in electroluminescence images—0
A Scalable k-Medoids Clustering via Whale Optimization Algorithm—0
ASE: Anomaly Scoring Based Ensemble Learning for Imbalanced Datasets—0
A secondary immune response based on co-evolutive populations of agents for anomaly detection and characterization—0
A Self-Commissioning Edge Computing Method for Data-Driven Anomaly Detection in Power Electronic 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
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