  Many promising applications of supervised machine learning face hurdles in
the acquisition of labeled data in sufficient quantity and quality, creating an
expensive bottleneck. To overcome such limitations, techniques that do not
depend on ground truth labels have been studied, including weak supervision and
generative modeling. While these techniques would seem to be usable in concert,
improving one another, how to build an interface between them is not
well-understood. In this work, we propose a model fusing programmatic weak
supervision and generative adversarial networks and provide theoretical
justification motivating this fusion. The proposed approach captures discrete
latent variables in the data alongside the weak supervision derived label
estimate. Alignment of the two allows for better modeling of sample-dependent
accuracies of the weak supervision sources, improving the estimate of
unobserved labels. It is the first approach to enable data augmentation through
weakly supervised synthetic images and pseudolabels. Additionally, its learned
latent variables can be inspected qualitatively. The model outperforms baseline
weak supervision label models on a number of multiclass image classification
datasets, improves the quality of generated images, and further improves
end-model performance through data augmentation with synthetic samples.
