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Label quality issues, such as noisy labels and imbalanced classdistributions, have negative effects on model performance. Automaticreweighting methods identify problematic samples with label quality issues byrecognizing their negative effects on validation samples and assigning lowerweights to them. However, these methods fail to achieve satisfactoryperformance when the validation samples are of low quality. To tackle this, wedevelop Reweighter, a visual analysis tool for sample reweighting. Thereweighting relationships between validation samples and training samples aremodeled as a bipartite graph. Based on this graph, a validation sampleimprovement method is developed to improve the quality of validation samples.Since the automatic improvement may not always be perfect, a co-cluster-basedbipartite graph visualization is developed to illustrate the reweightingrelationships and support the interactive adjustments to validation samples andreweighting results. The adjustments are converted into the constraints of thevalidation sample improvement method to further improve validation samples. Wedemonstrate the effectiveness of Reweighter in improving reweighting resultsthrough quantitative evaluation and two case studies.

Interactive Reweighting for Mitigating Label Quality Issues