Paucity of large curated hand-labeled training data for every
domain-of-interest 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
has given a set of weak labeling functions. We validated our method on the
MNIST, Fashion MNIST, CIFAR 10 and SVHN datasets, and it outperformed many
state-of-the-art models. We conducted extensive experiments to study its
usefulness, as well as showed how the proposed ADP framework can be used for
transfer learning as well as multi-task learning, where data from two domains
are generated simultaneously using the framework along with the label
information. Our future work will involve understanding the theoretical
implications of this new framework from a game-theoretic perspective, as well
as explore the performance of the method on more complex datasets.

Adversarial Data Programming (ADP) presents an adversarial methodology to generate data and a curated aggregated label by using distant supervision signals in the form of weak labeling functions, which outperformed many state-of-the-art models on image datasets like MNIST, Fashion MNIST, CIFAR 10, and SVHN. The proposed ADP framework could also be extended for multi-task learning and transfer learning.