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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2826–2850 of 4856 papers

TitleStatusHype
A Study of Representational Properties of Unsupervised Anomaly Detection in Brain MRICode0
Lightning Fast Video Anomaly Detection via Adversarial Knowledge DistillationCode0
An Anomaly Detection Method for Satellites Using Monte Carlo Dropout—0
MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection—0
Composite Score for Anomaly Detection in Imbalanced Real-World Industrial Dataset—0
A Deep Learning Anomaly Detection Method in Textual Data—0
Meta-Learning for Automated Selection of Anomaly Detectors for Semi-Supervised Datasets—0
Learning Invariant Rules from Data for Interpretable Anomaly DetectionCode0
Detecting Anomalies using Generative Adversarial Networks on Images—0
CoMadOut -- A Robust Outlier Detection Algorithm based on CoMADCode0
FRE: A Fast Method For Anomaly Detection And Segmentation—0
An Adaptive Event-based Data Converter for Always-on Biomedical Applications at the Edge—0
Corn Yield Prediction based on Remotely Sensed Variables Using Variational Autoencoder and Multiple Instance Regression—0
A Novel Center-based Deep Contrastive Metric Learning Method for the Detection of Polymicrogyria in Pediatric Brain MRICode0
MGADN: A Multi-task Graph Anomaly Detection Network for Multivariate Time Series—0
Generalizable Industrial Visual Anomaly Detection with Self-Induction Vision Transformer—0
STGlow: A Flow-based Generative Framework with Dual Graphormer for Pedestrian Trajectory Prediction—0
Machine Learning Methods for Anomaly Detection in Nuclear Power Plant Power Transformers—0
Normal Reference Attention and Defective Feature Perception Network for Surface Defect Detection—0
Rare Yet Popular: Evidence and Implications from Labeled Datasets for Network Anomaly Detection—0
Pedestrian Spatio-Temporal Information Fusion For Video Anomaly Detection—0
SpectNet : End-to-End Audio Signal Classification Using Learnable SpectrogramsCode0
Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation—0
Heart Abnormality Detection from Heart Sound Signals using MFCC Feature and Dual Stream Attention Based Network—0
Dynamic Interactional And Cooperative Network For Shield Machine—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