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 1926–1950 of 4856 papers

TitleStatusHype
An Adaptive Event-based Data Converter for Always-on Biomedical Applications at the Edge—0
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types—0
A Framework for Verifiable and Auditable Federated Anomaly Detection—0
Anomaly Detection Dataset for Industrial Control Systems—0
Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study—0
Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions—0
Automated Anomaly Detection on European XFEL Klystrons—0
Evaluating the Capabilities of Multi-modal Reasoning Models with Synthetic Task Data—0
Evaluating the Effectiveness of Video Anomaly Detection in the Wild: Online Learning and Inference for Real-world Deployment—0
Federated Structured Sparse PCA for Anomaly Detection in IoT Networks—0
Anomaly Detection for an E-commerce Pricing System—0
Evaluation of a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery—0
Evaluation of an Anomaly Detector for Routers using Parameterizable Malware in an IoT Ecosystem—0
Evaluation of Color Anomaly Detection in Multispectral Images For Synthetic Aperture Sensing—0
Evaluation of Machine Learning-based Anomaly Detection Algorithms on an Industrial Modbus/TCP Data Set—0
Evaluation of Point Pattern Features for Anomaly Detection of Defect within Random Finite Set Framework—0
Automated, real-time hospital ICU emergency signaling: A field-level implementation—0
Evaluation Pipeline for systematically searching for Anomaly Detection Systems—0
Evaluation Strategy of Time-series Anomaly Detection with Decay Function—0
EvAn: Neuromorphic Event-based Anomaly Detection—0
EVA-S2PLoR: A Secure Element-wise Multiplication Meets Logistic Regression on Heterogeneous Database—0
Event2Graph: Event-driven Bipartite Graph for Multivariate Time-series Anomaly Detection—0
Event and Anomaly Detection Using Tucker3 Decomposition—0
Event-Based Dynamic Banking Network Exploration for Economic Anomaly Detection—0
Crowd Scene Analysis using Deep Learning Techniques—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