Our primary motivation in studying GANs this semester was to try to apply a GAN-derived model to the generation of novel art. Much of the work in deep learning that has concerned itself with art generation has focused on style, and specifically the style of particular art pieces. In papers such as A Neural Algorithm of Artistic Style, deep learning nets learn to 1) differentiate the style of a piece of art from its content and 2) to apply that style to other content representations. By building off of the GAN model, we hoped to build a deep-net that was capable of not only learning a distribution of the style and content components of many different pieces of art, but was also able to novelly combine these components to create new pieces of art. The task of novel content generation is much more difficult than applying the style from one particular piece of art to the content of another.