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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

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Showing 3551–3575 of 4856 papers

TitleStatusHype
Anomaly Detection in Particulate Matter Sensor using Hypothesis Pruning Generative Adversarial Network—0
Anomaly Detection in Power Generation Plants with Generative Adversarial Networks—0
Anomaly Detection in Power Grids via Context-Agnostic Learning—0
Anomaly Detection in Power Markets and Systems—0
Anomaly Detection in Predictive Maintenance: A New Evaluation Framework for Temporal Unsupervised Anomaly Detection Algorithms—0
Anomaly Detection in Radar Data Using PointNets—0
Anomaly detection in reconstructed quantum states using a machine-learning technique—0
Anomaly Detection in Residential Video Surveillance on Edge Devices in IoT Framework—0
Anomaly Detection in Retinal Images using Multi-Scale Deep Feature Sparse Coding—0
Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey—0
Anomaly Detection in Satellite Videos using Diffusion Models—0
Anomaly Detection in Smart Power Grids with Graph-Regularized MS-SVDD: a Multimodal Subspace Learning Approach—0
Anomaly detection in static networks using egonets—0
Anomaly Detection in the Presence of Missing Values—0
Anomaly Detection in Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning—0
Anomaly Detection in Time Series of EDFA Pump Currents to Monitor Degeneration Processes using Fuzzy Clustering—0
Anomaly Detection in Traffic Scenes via Spatial-aware Motion Reconstruction—0
Anomaly Detection in Trajectory Data with Normalizing Flows—0
Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art—0
Anomaly Detection in Unstructured Environments using Bayesian Nonparametric Scene Modeling—0
Anomaly Detection in Unsupervised Surveillance Setting Using Ensemble of Multimodal Data with Adversarial Defense—0
Anomaly Detection in Video Data Based on Probabilistic Latent Space Models—0
Anomaly Detection in Video Sequence With Appearance-Motion Correspondence—0
Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks—0
Anomaly detection in video with Bayesian nonparametrics—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