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Crowdsourcing provides an efficient label collection schema for supervisedmachine learning. However, to control annotation cost, each instance in thecrowdsourced data is typically annotated by a small number of annotators. Thiscreates a sparsity issue and limits the quality of machine learning modelstrained on such data. In this paper, we study how to handle sparsity incrowdsourced data using data augmentation. Specifically, we propose to directlylearn a classifier by augmenting the raw sparse annotations. We implement twoprinciples of high-quality augmentation using Generative Adversarial Networks:1) the generated annotations should follow the distribution of authentic ones,which is measured by a discriminator; 2) the generated annotations should havehigh mutual information with the ground-truth labels, which is measured by anauxiliary network. Extensive experiments and comparisons against an array ofstate-of-the-art learning from crowds methods on three real-world datasetsproved the effectiveness of our data augmentation framework. It shows thepotential of our algorithm for low-budget crowdsourcing in general.

Improve Learning from Crowds via Generative Augmentation