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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2101–2125 of 4856 papers

TitleStatusHype
Convolutional Recurrent Reconstructive Network for Spatiotemporal Anomaly Detection in Solder Paste Inspection—0
Convolutional Neural Network for Multipath Detection in GNSS Receivers—0
Anomaly Detection via Multi-Scale Contrasted Memory—0
Convolutional Neural Network Design and Evaluation for Real-Time Multivariate Time Series Fault Detection in Spacecraft Attitude Sensors—0
Convolutional Graph Auto-encoder: A Deep Generative Neural Architecture for Probabilistic Spatio-temporal Solar Irradiance Forecasting—0
A deep learning-based approach for the automated surface inspection of copper clad laminate images—0
A2B-GAN: Utilizing Unannotated Anomalous Images for Anomaly Detection in Medical Image Analysis—0
Generalizable Industrial Visual Anomaly Detection with Self-Induction Vision Transformer—0
General Time-series Model for Universal Knowledge Representation of Multivariate Time-Series data—0
Convolutional Ensembling based Few-Shot Defect Detection Technique—0
Convolutional Autoencoders for Data Compression and Anomaly Detection in Small Satellite Technologies—0
Anomaly Detection via Minimum Likelihood Generative Adversarial Networks—0
Control theoretically explainable application of autoencoder methods to fault detection in nonlinear dynamic systems—0
Controllable RANSAC-based Anomaly Detection via Hypothesis Testing—0
Anomaly Detection via Learning-Based Sequential Controlled Sensing—0
A Deep Learning Approach to Video Anomaly Detection using Convolutional Autoencoders—0
Contrastive Structured Anomaly Detection for Gaussian Graphical Models—0
Anomaly Detection via Gumbel Noise Score Matching—0
Contrastive-Regularized U-Net for Video Anomaly Detection—0
A Multi-Level Approach for Class Imbalance Problem in Federated Learning for Remote Industry 4.0 Applications—0
A Combination of Temporal Sequence Learning and Data Description for Anomaly-based NIDS—0
GenCLIP: Generalizing CLIP Prompts for Zero-shot Anomaly Detection—0
Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Data—0
Contrastive Predictive Coding for Anomaly Detection—0
Anomaly Detection via Federated 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