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 1976–2000 of 4856 papers

TitleStatusHype
uTRAND: Unsupervised Anomaly Detection in Traffic Trajectories—0
Warped Time Series Anomaly Detection—0
Anomaly Correction of Business Processes Using Transformer Autoencoder—0
Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark—0
Integrating Graph Neural Networks with Scattering Transform for Anomaly Detection—0
CARE to Compare: A real-world dataset for anomaly detection in wind turbine data—0
Explainable Online Unsupervised Anomaly Detection for Cyber-Physical Systems via Causal Discovery from Time SeriesCode0
Polynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices ApproachCode0
Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection—0
Machine learning-based identification of Gaia astrometric exoplanet orbitsCode0
Reap the Wild Wind: Detecting Media Storms in Large-Scale News Corpora—0
Fault Detection in Mobile Networks Using Diffusion Models—0
Label-free Anomaly Detection in Aerial Agricultural Images with Masked Image Modeling—0
HCL-MTSAD: Hierarchical Contrastive Consistency Learning for Accurate Detection of Industrial Multivariate Time Series Anomalies—0
Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detection—0
Language Models Meet Anomaly Detection for Better Interpretability and GeneralizabilityCode0
Context-aware Video Anomaly Detection in Long-Term Datasets—0
Anomaly Detection in Power Grids via Context-Agnostic Learning—0
3D-CSAD: Untrained 3D Anomaly Detection for Complex Manufacturing Surfaces—0
Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability—0
Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey—0
EB-GAME: A Game-Changer in ECG Heartbeat Anomaly Detection—0
TimeCSL: Unsupervised Contrastive Learning of General Shapelets for Explorable Time Series Analysis—0
Anomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis Through Self-Supervised Learning—0
A Deep Look Into -- Automated Lung X-Ray Abnormality Detection System—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