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 3826–3850 of 4856 papers

TitleStatusHype
Self-awareness in intelligent vehicles: Feature based dynamic Bayesian models for abnormality detection—0
Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection—0
A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata—0
LSTM for Model-Based Anomaly Detection in Cyber-Physical Systems—0
Collective Awareness for Abnormality Detection in Connected Autonomous Vehicles—0
Interpretable Machine Learning Models for Predicting and Explaining Vehicle Fuel Consumption Anomalies—0
Self-awareness in Intelligent Vehicles: Experience Based Abnormality Detection—0
Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder—0
Dynamic Bayesian Approach for decision-making in Ego-Things—0
Smart Anomaly Detection in Sensor Systems: A Multi-Perspective Review—0
Enhanced Cyber-Physical Security Using Attack-resistant Cyber Nodes and Event-triggered Moving Target Defence—0
Model Extraction Attacks on Graph Neural Networks: Taxonomy and RealizationCode0
Low-rank on Graphs plus Temporally Smooth Sparse Decomposition for Anomaly Detection in Spatiotemporal Data—0
Network Anomaly Detection Using Federated Learning and Transfer Learning—0
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health EpisodesCode0
Anomaly Detection in a Large-scale Cloud Platform—0
Automating Abnormality Detection in Musculoskeletal Radiographs through Deep Learning—0
Anomaly Detection for Multivariate Time Series of Exotic Supernovae—0
A Federated Learning Approach to Anomaly Detection in Smart Buildings—0
Graph Fairing Convolutional Networks for Anomaly DetectionCode0
An Empirical Investigation of Contextualized Number PredictionCode0
Action Sequence Augmentation for Early Graph-based Anomaly DetectionCode0
Anomaly Detection on X-Rays Using Self-Supervised Aggregation Learning—0
Addressing Variance Shrinkage in Variational Autoencoders using Quantile Regression—0
On the Usage of Generative Models for Network Anomaly Detection in Multivariate Time-Series—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