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 4701–4725 of 4856 papers

TitleStatusHype
Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges—0
Graph Anomaly Detection with Noisy Labels by Reinforcement Learning—0
Graph-Based Method for Anomaly Prediction in Brain Network—0
Graph Anomaly Detection in Time Series: A Survey—0
Graph Coding for Model Selection and Anomaly Detection in Gaussian Graphical Models—0
GraphDART: Graph Distillation for Efficient Advanced Persistent Threat Detection—0
Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection—0
Graph-Level Embedding for Time-Evolving Graphs—0
Graph Mixture of Experts and Memory-augmented Routers for Multivariate Time Series Anomaly Detection—0
Graph neural network-based lithium-ion battery state of health estimation using partial discharging curve—0
Graph Pre-Training Models Are Strong Anomaly Detectors—0
GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection—0
Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection—0
GraphSAC: Detecting anomalies in large-scale graphs—0
Graph Sanitation with Application to Node Classification—0
GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network—0
Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis—0
Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection—0
Grid Monitoring with Synchro-Waveform and AI Foundation Model Technologies—0
Group Anomaly Detection using Flexible Genre Models—0
Grouped Convolutional Neural Networks for Multivariate Time Series—0
Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection—0
Guarding the Grid: Enhancing Resilience in Automated Residential Demand Response Against False Data Injection Attacks—0
HaloAE: An HaloNet based Local Transformer Auto-Encoder for Anomaly Detection and Localization—0
Hankel-structured Tensor Robust PCA for Multivariate Traffic Time Series Anomaly Detection—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