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 1751–1775 of 4856 papers

TitleStatusHype
Analytics and Machine Learning Powered Wireless Network Optimization and Planning—0
Evaluating the Capabilities of Multi-modal Reasoning Models with Synthetic Task Data—0
Evaluating the Effectiveness of Video Anomaly Detection in the Wild: Online Learning and Inference for Real-world Deployment—0
Dance With Self-Attention: A New Look of Conditional Random Fields on Anomaly Detection in Videos—0
Few-shot 1/a Anomalies Feedback : Damage Vision Mining Opportunity and Embedding Feature Imbalance—0
Anomaly-Injected Deep Support Vector Data Description for Text Outlier Detection—0
DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection—0
Dual-encoder Bidirectional Generative Adversarial Networks for Anomaly Detection—0
Analytical Probability Distributions and EM-Learning for Deep Generative Networks—0
Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation—0
DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation—0
A Survey on Social Media Anomaly Detection—0
Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection—0
Analytical Probability Distributions and Exact Expectation-Maximization for Deep Generative Networks—0
A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images—0
Dual-stream spatiotemporal networks with feature sharing for monitoring animals in the home cage—0
DACR: Distribution-Augmented Contrastive Reconstruction for Time-Series Anomaly Detection—0
ADEPOS: Anomaly Detection based Power Saving for Predictive Maintenance using Edge Computing—0
DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Network—0
Dynamic Bayesian Approach for decision-making in Ego-Things—0
Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study—0
Anomaly Generation using Generative Adversarial Networks in Host Based Intrusion Detection—0
Analytical Discovery of Manifold with Machine Learning—0
CyberSentinel: An Emergent Threat Detection System for AI Security—0
Cybersecurity threat detection based on a UEBA framework using Deep Autoencoders—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