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 4001–4025 of 4856 papers

TitleStatusHype
Physics-Driven AI Correction in Laser Absorption Sensing Quantification—0
Physics-Informed Convolutional Autoencoder for Cyber Anomaly Detection in Power Distribution Grids—0
Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward—0
PIAD: Pose and Illumination agnostic Anomaly Detection—0
PiDAn: A Coherence Optimization Approach for Backdoor Attack Detection and Mitigation in Deep Neural Networks—0
PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities—0
PIF: Anomaly detection via preference embedding—0
P-KDGAN: Progressive Knowledge Distillation with GANs for One-class Novelty Detection—0
Place-specific Background Modeling Using Recursive Autoencoders—0
Plug-and-Play Anomaly Detection with Expectation Maximization Filtering—0
Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection—0
PNUNet: Anomaly Detection using Positive-and-Negative Noise based on Self-Training Procedure—0
PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection—0
POET: A Self-learning Framework for PROFINET Industrial Operations Behaviour—0
Point Cloud Video Anomaly Detection Based on Point Spatio-Temporal Auto-Encoder—0
Polyra Swarms: A Shape-Based Approach to Machine Learning—0
Population Anomaly Detection through Deep Gaussianization—0
Portraying the Need for Temporal Data in Flood Detection via Sentinel-1—0
Position: Untrained Machine Learning for Anomaly Detection—0
Positive-Unlabeled Node Classification with Structure-aware Graph Learning—0
Post-Hoc Calibrated Anomaly Detection—0
Postulating Exoplanetary Habitability via a Novel Anomaly Detection Method—0
Practical Anomaly Detection over Multivariate Monitoring Metrics for Online Services—0
Practical Applications of Advanced Cloud Services and Generative AI Systems in Medical Image Analysis—0
Practical Machine Learning for Cloud Intrusion Detection: Challenges and the Way Forward—0
Show:102550
← PrevPage 161 of 195Next →
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