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 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. How can we get the most out of data when we do not have ground truth labels? Two prominent paradigms operate in this setting. First, programmatic weak supervision frameworks use weak sources of training signal to train downstream supervised models, without needing access to groundtruth labels (Riedel et al., 2010; Ratner et al., 2016; Dehghani et al., 2017; Lang & Poon, 2021). Second, generative models enable learning data distributions which can benefit downstream tasks, e.g. Intuitively, these two paradigms should complement each other, as each can be thought of as a different approach to extracting structure from unlabeled data. However, to date there is no simple way to combine them. Fusing generative models with weak supervision holds substantial promise. For example, it could yield large reductions in data acquisition costs for training complex models. Programmatic weak supervision replaces the need for manual annotations by applying so-called labeling functions to unlabeled data, producing weak labels that are combined into a pseudolabel for each sample.