2024
Prediction Consistency Regularization for Learning with Noise Labels Based on Contrastive Clustering
Sun X, Zhang S, Ma S. Prediction Consistency Regularization for Learning with Noise Labels Based on Contrastive Clustering. Entropy 2024, 26: 308. PMID: 38667864, PMCID: PMC11049179, DOI: 10.3390/e26040308.Peer-Reviewed Original ResearchLabel noiseContrastive clusteringConsistency regularizationRegularization termPrediction consistencyClassification accuracyImpact of label noiseEffects of label noiseClassification taskClustering problemComprehensive experimentsNoise labelsLabel informationNeural networkClustering resultsSample recognitionNoise rateMitigate noiseNoiseClassificationModel performanceRegularizationPrototypeAccuracyLabeling
2022
Deep unsupervised feature selection by discarding nuisance and correlated features
Shaham U, Lindenbaum O, Svirsky J, Kluger Y. Deep unsupervised feature selection by discarding nuisance and correlated features. Neural Networks 2022, 152: 34-43. PMID: 35500458, PMCID: PMC9526895, DOI: 10.1016/j.neunet.2022.04.002.Peer-Reviewed Original ResearchMeSH KeywordsCluster AnalysisConceptsUnsupervised feature selectionCorrelated featuresNuisance featuresFeature selectionReal-world datasetsAutoencoder architectureComplete featuresClustering resultsGraph LaplacianArchitectural designModern datasetsOptimization processDatasetDifferentiable approachUnderlying structureLarge numberSimilar approachFeaturesArchitectureLarge subsetSelectionCode
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