  Generating novel pairs of image and text is a problem that combines computer
vision and natural language processing. In this paper, we present strategies
for generating novel image and caption pairs based on existing captioning
datasets. The model takes advantage of recent advances in generative
adversarial networks and sequence-to-sequence modeling. We make generalizations
to generate paired samples from multiple domains. Furthermore, we study cycles
-- generating from image to text then back to image and vise versa, as well as
its connection with autoencoders.
