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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2576–2600 of 4856 papers

TitleStatusHype
Semi-supervised Anomaly Detection with Extremely Limited Labels in Dynamic Graphs—0
Semi-Supervised Bone Marrow Lesion Detection from Knee MRI Segmentation Using Mask Inpainting Models—0
Semi-Supervised Health Index Monitoring with Feature Generation and Fusion—0
Semi-Supervised Learning of Bearing Anomaly Detection via Deep Variational Autoencoders—0
Semi-supervised learning via DQN for log anomaly detection—0
Semi-Supervised Surface Anomaly Detection of Composite Wind Turbine Blades From Drone Imagery—0
Semi-supervised Variational Temporal Convolutional Network for IoT Communication Multi-anomaly Detection—0
Deep Anomaly Discovery From Unlabeled Videos via Normality Advantage and Self-Paced Refinement—0
Sensitivity Estimation for Dark Matter Subhalos in Synthetic Gaia DR2 using Deep Learning—0
SentinelAgent: Graph-based Anomaly Detection in Multi-Agent Systems—0
Sentinel: Scheduling Live Streams with Proactive Anomaly Detection in Crowdsourced Cloud-Edge Platforms—0
Separating Novel Features for Logical Anomaly Detection: A Straightforward yet Effective Approach—0
Seq-HyGAN: Sequence Classification via Hypergraph Attention Network—0
Sequence Aggregation Rules for Anomaly Detection in Computer Network Traffic—0
Sequential Anomaly Detection using Inverse Reinforcement Learning—0
Sequential Deep Learning Architectures for Anomaly Detection in Virtual Network Function Chains—0
Sequential Feature Explanations for Anomaly Detection—0
Sequential online prediction in the presence of outliers and change points: an instant temporal structure learning approach—0
Sequential PatchCore: Anomaly Detection for Surface Inspection using Synthetic Impurities—0
A Joint Model for IT Operation Series Prediction and Anomaly Detection—0
Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series—0
Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detection—0
SHEDAD: SNN-Enhanced District Heating Anomaly Detection for Urban Substations—0
Should I Raise The Red Flag? A comprehensive survey of anomaly scoring methods toward mitigating false alarms—0
si4onnx: A Python package for Selective Inference in Deep Learning Models—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