Generative Modeling Helps Weak Supervision (and Vice Versa)

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 developed, 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 weak supervision and generative adversarial networks. It captures discrete variables in the data alongside the weak supervision derived label estimate. Their alignment allows for better modeling of sample-dependent accuracies of the weak supervision sources, improving the unobserved ground truth estimate. It is the first approach to enable data augmentation through weakly supervised synthetic images and pseudolabels. Additionally, its learned discrete variables can be inspected qualitatively. The model outperforms baseline weak supervision label models on a number of multiclass classification datasets, improves the quality of generated images, and further improves end-model performance through data augmentation with synthetic samples.