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 1851–1875 of 4856 papers

TitleStatusHype
Anomaly Detection with Tensor Networks—0
Enabling Machine Learning Across Heterogeneous Sensor Networks with Graph Autoencoders—0
A Maritime Industry Experience for Vessel Operational Anomaly Detection: Utilizing Deep Learning Augmented with Lightweight Interpretable Models—0
A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories—0
CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detection—0
Curved Geometric Networks for Visual Anomaly Recognition—0
End-to-End Abnormality Detection in Medical Imaging—0
End-To-End Anomaly Detection for Identifying Malicious Cyber Behavior through NLP-Based Log Embeddings—0
End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection—0
End-to-End Convolutional Activation Anomaly Analysis for Anomaly Detection—0
Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing—0
Energy-Based Anomaly Detection and Localization—0
Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach—0
Energy-based Models for Video Anomaly Detection—0
Energy-Efficient Classification for Anomaly Detection: The Wireless Channel as a Helper—0
Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants—0
Anomaly detection with semi-supervised classification based on risk estimators—0
Enforcing Cybersecurity Constraints for LLM-driven Robot Agents for Online Transactions—0
EnGAN: Latent Space MCMC and Maximum Entropy Generators for Energy-based Models—0
Engineering Risk-Aware, Security-by-Design Frameworks for Assurance of Large-Scale Autonomous AI Models—0
An AI-Based Public Health Data Monitoring System—0
FadMan: Federated Anomaly Detection across Multiple Attributed Networks—0
A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection—0
Enhanced Cyber-Physical Security through Deep Learning Techniques—0
CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimation—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