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 2926–2950 of 4856 papers

TitleStatusHype
Unlocking Multimodal Integration in EHRs: A Prompt Learning Framework for Language and Time Series Fusion—0
Unmanned Aerial System Security using Real-time Autopilot Software Analysis—0
Unmasking the abnormal events in video—0
Unraveling Attacks in Machine Learning-based IoT Ecosystems: A Survey and the Open Libraries Behind Them—0
Unraveling the Complexity of Splitting Sequential Data: Tackling Challenges in Video and Time Series Analysis—0
Unravelling physics beyond the standard model with classical and quantum anomaly detection—0
Unseen Visual Anomaly Generation—0
Unsupervised 3D Brain Anomaly Detection—0
Unsupervised Abnormality Detection through Mixed Structure Regularization (MSR) in Deep Sparse Autoencoders—0
Unsupervised Abnormality Detection Using Heterogeneous Autonomous Systems—0
Unsupervised Abnormal Traffic Detection through Topological Flow Analysis—0
Unsupervised Adversarial Anomaly Detection using One-Class Support Vector Machines—0
Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networks—0
Unsupervised Anomalous Data Space Specification—0
Unsupervised Anomalous Trajectory Detection for Crowded Scenes—0
Unsupervised Anomaly and Change Detection with Multivariate Gaussianization—0
Unsupervised Anomaly Detection and Localization with Generative Adversarial Networks—0
Unsupervised anomaly detection for a Smart Autonomous Robotic Assistant Surgeon (SARAS)using a deep residual autoencoder—0
Unsupervised anomaly detection for discrete sequence healthcare data—0
Unsupervised Anomaly Detection for Tabular Data Using Noise Evaluation—0
Unsupervised Anomaly Detection From Semantic Similarity Scores—0
Unsupervised Anomaly Detection from Time-of-Flight Depth Images—0
Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with Multi-Task Brain Age Prediction—0
Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with impured training data—0
Unsupervised anomaly detection in digital pathology using GANs—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