  The paucity of large curated hand-labeled training data forms a major
bottleneck in the deployment of machine learning models in computer vision and
other fields. Recent work (Data Programming) has shown how distant supervision
signals in the form of labeling functions can be used to obtain labels for
given data in near-constant time. In this work, we present Adversarial Data
Programming (ADP), which presents an adversarial methodology to generate data
as well as a curated aggregated label, given a set of weak labeling functions.
More interestingly, such labeling functions are often easily generalizable,
thus allowing our framework to be extended to different setups, including
self-supervised labeled image generation, zero-shot text to labeled image
generation, transfer learning, and multi-task learning.
