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 2801–2825 of 4856 papers

TitleStatusHype
Spatial-Temporal Anomaly Detection for Sensor Attacks in Autonomous Vehicles—0
Cross-Domain Video Anomaly Detection without Target Domain Adaptation—0
Mul-GAD: a semi-supervised graph anomaly detection framework via aggregating multi-view informationCode0
Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series—0
Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection—0
Transformer-based normative modelling for anomaly detection of early schizophrenia—0
Unsupervised Anomaly Detection in Time-series: An Extensive Evaluation and Analysis of State-of-the-art Methods—0
AIDA: Analytic Isolation and Distance-based Anomaly Detection AlgorithmCode0
Prototypical Residual Networks for Anomaly Detection and Localization—0
Continual learning on deployment pipelines for Machine Learning Systems—0
FEMa-FS: Finite Element Machines for Feature Selection—0
Lossy Compression for Robust Unsupervised Time-Series Anomaly Detection—0
Anomaly Detection in Power Markets and Systems—0
A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems—0
A Hybrid Deep Learning Anomaly Detection Framework for Intrusion Detection—0
Application of a Dynamic Line Graph Neural Network for Intrusion Detection With Semisupervised Learning—0
Crowd-level Abnormal Behavior Detection via Multi-scale Motion Consistency Learning—0
Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View—0
Interpreting Vulnerabilities of Multi-Instance Learning to Adversarial PerturbationsCode0
Automated anomaly-aware 3D segmentation of bones and cartilages in knee MR images from the Osteoarthritis InitiativeCode0
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch—0
Unsupervised Visual Defect Detection with Score-Based Generative Model—0
G-CMP: Graph-enhanced Contextual Matrix Profile for unsupervised anomaly detection in sensor-based remote health monitoring—0
Novelty Detection for Election Fraud: A Case Study with Agent-Based Simulation Data—0
ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awareness—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