  Crowdsourcing provides an efficient label collection schema for supervised
machine learning. However, to control annotation cost, each instance in the
crowdsourced data is typically annotated by a small number of annotators. This
creates a sparsity issue and limits the quality of machine learning models
trained on such data. In this paper, we study how to handle sparsity in
crowdsourced data using data augmentation. Specifically, we propose to directly
learn a classifier by augmenting the raw sparse annotations. We implement two
principles 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 have
high mutual information with the ground-truth labels, which is measured by an
auxiliary network. Extensive experiments and comparisons against an array of
state-of-the-art learning from crowds methods on three real-world datasets
proved the effectiveness of our data augmentation framework. It shows the
potential of our algorithm for low-budget crowdsourcing in general.
