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 676–700 of 4856 papers

TitleStatusHype
On-device Anomaly Detection in Conveyor Belt Operations—0
Steam Turbine Anomaly Detection: An Unsupervised Learning Approach Using Enhanced Long Short-Term Memory Variational Autoencoder—0
Take Package as Language: Anomaly Detection Using TransformerCode0
Outliers resistant image classification by anomaly detection—0
A Hybrid Artificial Intelligence System for Automated EEG Background Analysis and Report GenerationCode0
Deep Autoencoders for Unsupervised Anomaly Detection in Wildfire Prediction—0
Exploring Zero-Shot Anomaly Detection with CLIP in Medical Imaging: Are We There Yet?—0
Adaptive Deviation Learning for Visual Anomaly Detection with Data ContaminationCode0
Continuous GNN-based Anomaly Detection on Edge using Efficient Adaptive Knowledge Graph Learning—0
AI-Enhanced Inverter Fault and Anomaly Detection System for Distributed Energy Resources in Microgrids—0
A Fuzzy Reinforcement LSTM-based Long-term Prediction Model for Fault Conditions in Nuclear Power Plants—0
AstroM^3: A self-supervised multimodal model for astronomy—0
Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and BenchmarksCode2
Weakly-Supervised Anomaly Detection in Surveillance Videos Based on Two-Stream I3D Convolution Network—0
Anomaly Detection in Large-Scale Cloud Systems: An Industry Case and DatasetCode0
LogLLM: Log-based Anomaly Detection Using Large Language ModelsCode2
EAPCR: A Universal Feature Extractor for Scientific Data without Explicit Feature Relation Patterns—0
Disentangling Tabular Data Towards Better One-Class Anomaly DetectionCode0
Spatially Regularized Graph Attention Autoencoder Framework for Detecting Rainfall Extremes—0
Contrastive Language Prompting to Ease False Positives in Medical Anomaly DetectionCode0
Anomaly Detection in OKTA Logs using Autoencoders—0
A neural-network based anomaly detection system and a safety protocol to protect vehicular network—0
Enhancing Predictive Maintenance in Mining Mobile Machinery through a TinyML-enabled Hierarchical Inference Network—0
Locally Adaptive One-Class Classifier Fusion with Dynamic p-Norm Constraints for Robust Anomaly Detection—0
UniGAD: Unifying Multi-level Graph Anomaly DetectionCode1
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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