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 2851–2875 of 4856 papers

TitleStatusHype
On the Connection of Generative Models and Discriminative Models for Anomaly Detection—0
Anomaly Detection via Multi-Scale Contrasted Memory—0
LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly DetectionCode0
Cyber-Attack Event Analysis for EV Charging Stations—0
Attacking Face Recognition with T-shirts: Database, Vulnerability Assessment and Detection—0
Detecting Disengagement in Virtual Learning as an Anomaly using Temporal Convolutional Network Autoencoder—0
A Comprehensive Survey of Transformers for Computer Vision—0
Computer Vision on X-ray Data in Industrial Production and Security Applications: A Comprehensive Survey—0
Evaluation of Color Anomaly Detection in Multispectral Images For Synthetic Aperture Sensing—0
Quantum-probabilistic Hamiltonian learning for generative modelling & anomaly detectionCode0
Privacy-Preserving Anomaly Detection in Stochastic Dynamical Systems: Synthesis of Optimal Gaussian Mechanisms—0
Degradation Prediction of Semiconductor Lasers using Conditional Variational Autoencoder—0
A Machine Learning-based Framework for Predictive Maintenance of Semiconductor Laser for Optical Communication—0
Deep learning for structural health monitoring: An application to heritage structures—0
Discussion of Features for Acoustic Anomaly Detection under Industrial Disturbing Noise in an End-of-Line Test of Geared Motors—0
Generation of Anonymous Chest Radiographs Using Latent Diffusion Models for Training Thoracic Abnormality Classification Systems—0
DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Network—0
Exploiting Spatial-temporal Correlations for Video Anomaly Detection—0
HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection—0
Unsupervised Anomaly Detection of Paranasal Anomalies in the Maxillary Sinus—0
Siamese Transition Masked Autoencoders as Uniform Unsupervised Visual Anomaly Detector—0
Teacher-Student Network for 3D Point Cloud Anomaly Detection with Few Normal Samples—0
SoK: Modeling Explainability in Security Analytics for Interpretability, Trustworthiness, and Usability—0
Classical ensemble of Quantum-classical ML algorithms for Phishing detection in Ethereum transaction networksCode0
Joint Sub-component Level Segmentation and Classification for Anomaly Detection within Dual-Energy X-Ray Security Imagery—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