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 2776–2800 of 4856 papers

TitleStatusHype
Time series Forecasting to detect anomalous behaviours in Multiphase Flow Meters—0
Label-Efficient Interactive Time-Series Anomaly Detection—0
Anomaly detection in laser-guided vehicles' batteries: a case study—0
PRISM: Privacy Preserving Healthcare Internet of Things Security Management—0
Application of Unsupervised Domain Adaptation for Structural MRI Analysis—0
A Novel Self-Supervised Learning-Based Anomaly Node Detection Method Based on an Autoencoder in Wireless Sensor NetworksCode0
Understanding Ethics, Privacy, and Regulations in Smart Video Surveillance for Public Safety—0
EndoBoost: a plug-and-play module for false positive suppression during computer-aided polyp detection in real-world colonoscopy (with dataset)—0
Mesh of Things (MoT) Network-Driven Anomaly Detection in Connected Objects—0
Anomaly Detection using Ensemble Classification and Evidence Theory—0
Supervised Anomaly Detection Method Combining Generative Adversarial Networks and Three-Dimensional Data in Vehicle Inspections—0
Machine Learning with Probabilistic Law Discovery: A Concise Introduction—0
Joint Embedding of 2D and 3D Networks for Medical Image Anomaly Detection—0
LogAnMeta: Log Anomaly Detection Using Meta Learning—0
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time seriesCode0
Continual Learning Approaches for Anomaly DetectionCode0
Scene Change Detection Using Multiscale Cascade Residual Convolutional Neural Networks—0
Video Segmentation Learning Using Cascade Residual Convolutional Neural Network—0
Resonant Anomaly Detection with Multiple Reference Datasets—0
Privacy-Protecting Behaviours of Risk Detection in People with Dementia using Videos—0
Weakly Supervised Video Anomaly Detection Based on Cross-Batch Clustering Guidance—0
DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection—0
A scalable framework for annotating photovoltaic cell defects in electroluminescence images—0
EVAL: Explainable Video Anomaly Localization—0
Anomaly Detection in Driving by Cluster Analysis Twice—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