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 3226–3250 of 4856 papers

TitleStatusHype
Augmentation based unsupervised domain adaptation—0
Deep Graph Learning for Anomalous Citation Detection—0
ML-based Anomaly Detection in Optical Fiber Monitoring—0
Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization—0
Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants—0
Recurrent Auto-Encoder With Multi-Resolution Ensemble and Predictive Coding for Multivariate Time-Series Anomaly Detection—0
ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variables—0
A Novel Anomaly Detection Method for Multimodal WSN Data Flow via a Dynamic Graph Neural Network—0
Anomalib: A Deep Learning Library for Anomaly Detection—0
Trustworthy Anomaly Detection: A Survey—0
Simulating Malicious Attacks on VANETs for Connected and Autonomous Vehicle Cybersecurity: A Machine Learning Dataset—0
Deep Learning-based Anomaly Detection on X-ray Images of Fuel Cell Electrodes—0
Federated-Learning-Based Anomaly Detection for IoT Security Attacks—0
DeCorus: Hierarchical Multivariate Anomaly Detection at Cloud-Scale—0
vue4logs -- Automatic Structuring of Heterogeneous Computer System Logs—0
Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos—0
RandomSEMO: Normality Learning Of Moving Objects For Video Anomaly Detection—0
An Automated Analysis Framework for Trajectory Datasets—0
Hybridization of Capsule and LSTM Networks for unsupervised anomaly detection on multivariate data—0
From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach—0
Detecting out-of-context objects using contextual cues—0
Meta-learning with GANs for anomaly detection, with deployment in high-speed rail inspection system—0
Two-Stage Deep Anomaly Detection with Heterogeneous Time Series Data—0
Identifying Backdoor Attacks in Federated Learning via Anomaly Detection—0
Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion—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