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 2151–2175 of 4856 papers

TitleStatusHype
Anomaly Detection Using One-Class SVM for Logs of Juniper Router Devices—0
GenIAS: Generator for Instantiating Anomalies in time Series—0
A Novel Spatiotemporal Correlation Anomaly Detection Method Based on Time-Frequency-Domain Feature Fusion and a Dynamic Graph Neural Network in Wireless Sensor Network—0
A Model-Free Kullback-Leibler Divergence Filter for Anomaly Detection in Noisy Data Series—0
Genetic Neural Architecture Search for automatic assessment of human sperm images—0
GFCL: A GRU-based Federated Continual Learning Framework against Data Poisoning Attacks in IoV—0
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection—0
Context-Dependent Anomaly Detection with Knowledge Graph Embedding Models—0
Anomaly Detection using Generative Models and Sum-Product Networks in Mammography Scans—0
Context-Dependent Anomaly Detection for Low Altitude Traffic Surveillance—0
Context-aware Video Anomaly Detection in Long-Term Datasets—0
‘Am I the Bad One’? Predicting the Moral Judgement of the Crowd Using Pre–trained Language Models—0
GiBy: A Giant-Step Baby-Step Classifier For Anomaly Detection In Industrial Control Systems—0
Global Spatio-Temporal Fusion-based Traffic Prediction Algorithm with Anomaly Aware—0
Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach—0
GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection—0
Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detection—0
Context-Aware Trajectory Anomaly Detection—0
Context-aware TFL: A Universal Context-aware Contrastive Learning Framework for Temporal Forgery Localization—0
Anomaly Detection Using GANs for Visual Inspection in Noisy Training Data—0
Context-Aware Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition—0
Context-aware Domain Adaptation for Time Series Anomaly Detection—0
Anomaly Detection using Ensemble Classification and Evidence Theory—0
Am I Rare? An Intelligent Summarization Approach for Identifying Hidden Anomalies—0
Anomaly Detection using Edge Computing in Video Surveillance System: Review—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