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 3601–3625 of 4856 papers

TitleStatusHype
Anomaly detection using data depth: multivariate case—0
Anomaly Detection using Deep Autoencoders for in-situ Wastewater Systems Monitoring Data—0
Anomaly Detection using Deep Learning based Image Completion—0
Anomaly Detection using Deep Reconstruction and Forecasting for Autonomous Systems—0
Anomaly detection using Diffusion-based methods—0
Anomaly Detection using Edge Computing in Video Surveillance System: Review—0
Anomaly Detection using Ensemble Classification and Evidence Theory—0
Anomaly Detection Using GANs for Visual Inspection in Noisy Training Data—0
Anomaly Detection using Generative Models and Sum-Product Networks in Mammography Scans—0
Anomaly Detection Using One-Class SVM for Logs of Juniper Router Devices—0
Anomaly Detection Using the Knowledge-based Temporal Abstraction Method—0
Anomaly Detection Utilizing a Riemann Metric for Robust Myoelectric Pattern Recognition—0
Anomaly Detection via Autoencoder Composite Features and NCE—0
Anomaly Detection via Controlled Sensing and Deep Active Inference—0
Anomaly Detection via Federated Learning—0
Anomaly Detection via Gumbel Noise Score Matching—0
Anomaly Detection via Learning-Based Sequential Controlled Sensing—0
Anomaly Detection via Minimum Likelihood Generative Adversarial Networks—0
Anomaly Detection via Multi-Scale Contrasted Memory—0
Anomaly Detection with Adversarially Learned Perturbations of Latent Space—0
Anomaly detection with a variational autoencoder for Arabic mispronunciation detection—0
Anomaly Detection With Conditional Variational Autoencoders—0
Anomaly Detection with Convolutional Autoencoders for Fingerprint Presentation Attack Detection—0
Anomaly Detection with Domain Adaptation—0
Anomaly Detection with Ensemble of Encoder and Decoder—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