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 2401–2425 of 4856 papers

TitleStatusHype
Anomaly Detection Models for IoT Time Series Data—0
High Dimensional Data Modeling Techniques for Detection of Chemical Plumes and Anomalies in Hyperspectral Images and Movies—0
Anomaly Detection in Time Series of EDFA Pump Currents to Monitor Degeneration Processes using Fuzzy Clustering—0
Higher-Order Moment-Based Anomaly Detection—0
Graph Coding for Model Selection and Anomaly Detection in Gaussian Graphical Models—0
Anomaly Detection of Command Shell Sessions based on DistilBERT: Unsupervised and Supervised Approaches—0
A Light-weight and Unsupervised Method for Near Real-time Behavioral Analysis using Operational Data Measurement—0
Hi-SAM: A high-scalable authentication model for satellite-ground Zero-Trust system using mean field game—0
Graph Anomaly Detection in Time Series: A Survey—0
History-based Anomaly Detector: an Adversarial Approach to Anomaly Detection—0
HLogformer: A Hierarchical Transformer for Representing Log Data—0
HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection—0
Hoi2Anomaly: An Explainable Anomaly Detection Approach Guided by Human-Object Interaction—0
Holistic Features For Real-Time Crowd Behaviour Anomaly Detection—0
Graph-Based Method for Anomaly Prediction in Brain Network—0
Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models—0
Improving log-based anomaly detection through learned adaptive filter—0
Chili Pepper Disease Diagnosis via Image Reconstruction Using GrabCut and Generative Adversarial Serial Autoencoder—0
Graph Anomaly Detection with Noisy Labels by Reinforcement Learning—0
HomographyAD: Deep Anomaly Detection Using Self Homography Learning—0
Host-based anomaly detection using Eigentraces feature extraction and one-class classification on system call trace data—0
How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?—0
Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges—0
Collective Awareness for Abnormality Detection in Connected Autonomous Vehicles—0
Anomaly Detection in Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning—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