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 3876–3900 of 4856 papers

TitleStatusHype
Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization—0
Noncontact Respiratory Anomaly Detection Using Infrared Light-Wave Sensing—0
Non-contact Sensing for Anomaly Detection in Wind Turbine Blades: A focus-SVDD with Complex-Valued Auto-Encoder Approach—0
No Need to Know Physics: Resilience of Process-based Model-free Anomaly Detection for Industrial Control Systems—0
Normality Addition via Normality Detection in Industrial Image Anomaly Detection Models—0
Normalizing flows for novelty detection in industrial time series data—0
No Shifted Augmentations (NSA): compact distributions for robust self-supervised Anomaly Detection—0
No Shifted Augmentations (NSA): strong baselines for self-supervised Anomaly Detection—0
Novel Applications for VAE-based Anomaly Detection Systems—0
Novel machine learning applications at the LHC—0
Novel semi-metrics for multivariate change point analysis and anomaly detection—0
Novelty Detection for Election Fraud: A Case Study with Agent-Based Simulation Data—0
N-pad : Neighboring Pixel-based Industrial Anomaly Detection—0
NUMOSIM: A Synthetic Mobility Dataset with Anomaly Detection Benchmarks—0
NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection—0
Object-centric and memory-guided normality reconstruction for video anomaly detection—0
Object-Centric Anomaly Detection by Attribute-Based Reasoning—0
Object Class Aware Video Anomaly Detection through Image Translation—0
Objective and Interpretable Breast Cosmesis Evaluation with Attention Guided Denoising Diffusion Anomaly Detection Model—0
OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations—0
Oddballness: universal anomaly detection with language models—0
ODE - Augmented Training Improves Anomaly Detection in Sensor Data from Machines—0
OIAD: One-for-all Image Anomaly Detection with Disentanglement Learning—0
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning—0
OmniAL: A Unified CNN Framework for Unsupervised Anomaly Localization—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