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 3101–3150 of 4856 papers

TitleStatusHype
CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms—0
A Personalized Federated Learning Algorithm: an Application in Anomaly Detection—0
Towards Smart Monitored AM: Open Source in-Situ Layer-wise 3D Printing Image Anomaly Detection Using Histograms of Oriented Gradients and a Physics-Based Rendering Engine—0
Automated, real-time hospital ICU emergency signaling: A field-level implementation—0
A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods—0
Markus Thill Temporal convolutional autoencoder for unsupervised anomaly detection in time series—0
TADPOLE: Task ADapted Pre-Training via AnOmaLy DEtection—0
Semantic Novelty Detection in Natural Language DescriptionsCode0
A Modified Dynamic Time Warping (MDTW) Approach and Innovative Average Non-Self Match Distance (ANSD) Method for Anomaly Detection in ECG Recordings—0
Real-Time Detection of Anomalies in Large-Scale Transient Surveys—0
Evaluation of an Anomaly Detector for Routers using Parameterizable Malware in an IoT Ecosystem—0
Boosting Anomaly Detection Using Unsupervised Diverse Test-Time AugmentationCode0
PEDENet: Image Anomaly Localization via Patch Embedding and Density Estimation—0
How to boost autoencoders?—0
Multi-Class Anomaly Detection—0
OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the BoundaryCode1
Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data ContaminationCode0
Anomaly-Injected Deep Support Vector Data Description for Text Outlier Detection—0
Sensing Anomalies as Potential Hazards: Datasets and BenchmarksCode1
Subtractive Aggregation for Attributed Network Anomaly DetectionCode0
A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future ChallengesCode1
Revisiting randomized choices in isolation forests—0
Patch vs. Global Image-Based Unsupervised Anomaly Detection in MR Brain Scans of Early Parkinsonian Patients—0
Practical Galaxy Morphology Tools from Deep Supervised Representation LearningCode1
Pediatric Otoscopy Video Screening with Shift Contrastive Anomaly Detection—0
Latent-Insensitive autoencoders for Anomaly Detection—0
Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review—0
Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator—0
Generalized Out-of-Distribution Detection: A SurveyCode1
Synthetic Temporal Anomaly Guided End-to-End Video Anomaly DetectionCode1
Learning Not to Reconstruct AnomaliesCode1
Memory-augmented Adversarial Autoencoders for Multivariate Time-series Anomaly Detection with Deep Reconstruction and Prediction—0
Anomaly Detection in Multi-Agent Trajectories for Automated DrivingCode1
A Semi-Supervised Approach for Abnormal Event Prediction on Large Operational Network Time-Series Data—0
Challenges for Unsupervised Anomaly Detection in Particle Physics—0
Deep Video Anomaly Detection: Opportunities and Challenges—0
A Survey on Proactive Customer Care: Enabling Science and Steps to Realize it—0
Multi-branch Neural Networks for Video Anomaly Detection in Adverse Lighting and Weather Conditions—0
Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization—0
Hankel-structured Tensor Robust PCA for Multivariate Traffic Time Series Anomaly Detection—0
Minimal-Configuration Anomaly Detection for IIoT Sensors—0
Anomaly Detection in Beehives: An Algorithm Comparison—0
AnoSeg: Anomaly Segmentation Network Using Self-Supervised Learning—0
Differential Anomaly Detection for Facial Images—0
Multivariate Anomaly Detection based on Prediction Intervals Constructed using Deep Learning—0
Generative Pre-Trained Transformer for Cardiac Abnormality Detection—0
Tribuo: Machine Learning with Provenance in JavaCode2
A Uniform Framework for Anomaly Detection in Deep Neural NetworksCode0
Anomaly Transformer: Time Series Anomaly Detection with Association DiscrepancyCode2
Fully Convolutional Cross-Scale-Flows for Image-based Defect 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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (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