  We investigate Generative Adversarial Networks (GANs) to model one particular
kind of image: frames from TV cartoons. Cartoons are particularly interesting
because their visual appearance emphasizes the important semantic information
about a scene while abstracting out the less important details, but each
cartoon series has a distinctive artistic style that performs this abstraction
in different ways. We consider a dataset consisting of images from two popular
television cartoon series, Family Guy and The Simpsons. We examine the ability
of GANs to generate images from each of these two domains, when trained
independently as well as on both domains jointly. We find that generative
models may be capable of finding semantic-level correspondences between these
two image domains despite the unsupervised setting, even when the training data
does not give labeled alignments between them.
