  Data augmentation is a ubiquitous technique for increasing the size of
labeled training sets by leveraging task-specific data transformations that
preserve class labels. While it is often easy for domain experts to specify
individual transformations, constructing and tuning the more sophisticated
compositions typically needed to achieve state-of-the-art results is a
time-consuming manual task in practice. We propose a method for automating this
process by learning a generative sequence model over user-specified
transformation functions using a generative adversarial approach. Our method
can make use of arbitrary, non-deterministic transformation functions, is
robust to misspecified user input, and is trained on unlabeled data. The
learned transformation model can then be used to perform data augmentation for
any end discriminative model. In our experiments, we show the efficacy of our
approach on both image and text datasets, achieving improvements of 4.0
accuracy points on CIFAR-10, 1.4 F1 points on the ACE relation extraction task,
and 3.4 accuracy points when using domain-specific transformation operations on
a medical imaging dataset as compared to standard heuristic augmentation
approaches.
