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 2801–2825 of 4856 papers

TitleStatusHype
Continual Learning for Anomaly Detection in Surveillance Videos—0
Continual learning on deployment pipelines for Machine Learning Systems—0
Continuous GNN-based Anomaly Detection on Edge using Efficient Adaptive Knowledge Graph Learning—0
Continuous Test-time Domain Adaptation for Efficient Fault Detection under Evolving Operating Conditions—0
Learning Informative Health Indicators Through Unsupervised Contrastive Learning—0
Contrastive Learning for Time Series on Dynamic Graphs—0
Contrastive Predictive Coding for Anomaly Detection—0
Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Data—0
Contrastive-Regularized U-Net for Video Anomaly Detection—0
Contrastive Structured Anomaly Detection for Gaussian Graphical Models—0
Probabilistic Segmentation for Robust Field of View Estimation—0
Probabilistic Software Modeling: A Data-driven Paradigm for Software Analysis—0
Process mining-driven modeling and simulation to enhance fault diagnosis in cyber-physical systems—0
Process Monitoring Using Maximum Sequence Divergence—0
Produce Once, Utilize Twice for Anomaly Detection—0
Progressing from Anomaly Detection to Automated Log Labeling and Pioneering Root Cause Analysis—0
Progressive GANomaly: Anomaly detection with progressively growing GANs—0
Projected Sliced Wasserstein Autoencoder-based Hyperspectral Images Anomaly Detection—0
Prototypes as Explanation for Time Series Anomaly Detection—0
Prototypical Residual Networks for Anomaly Detection and Localization—0
Proximally Sensitive Error for Anomaly Detection and Feature Learning—0
Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images—0
PS-DeVCEM: Pathology-sensitive deep learning model for video capsule endoscopy based on weakly labeled data—0
PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies—0
Pseudo Replay-based Class Continual Learning for Online New Category Anomaly Detection in Advanced Manufacturing—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