  Programmatic Weak Supervision (PWS) and generative models serve as crucial
tools that enable researchers to maximize the utility of existing datasets
without resorting to laborious data gathering and manual annotation processes.
PWS uses various weak supervision techniques to estimate the underlying class
labels of data, while generative models primarily concentrate on sampling from
the underlying distribution of the given dataset. Although these methods have
the potential to complement each other, they have mostly been studied
independently. Recently, WSGAN proposed a mechanism to fuse these two models.
Their approach utilizes the discrete latent factors of InfoGAN to train the
label model and leverages the class-dependent information of the label model to
generate images of specific classes. However, the disentangled latent factors
learned by InfoGAN might not necessarily be class-specific and could
potentially affect the label model's accuracy. Moreover, prediction made by the
label model is often noisy in nature and can have a detrimental impact on the
quality of images generated by GAN. In our work, we address these challenges by
(i) implementing a noise-aware classifier using the pseudo labels generated by
the label model (ii) utilizing the noise-aware classifier's prediction to train
the label model and generate class-conditional images. Additionally, we also
investigate the effect of training the classifier with a subset of the dataset
within a defined uncertainty budget on pseudo labels. We accomplish this by
formalizing the subset selection problem as a submodular maximization objective
with a knapsack constraint on the entropy of pseudo labels. We conduct
experiments on multiple datasets and demonstrate the efficacy of our methods on
several tasks vis-a-vis the current state-of-the-art methods.
