  We examined the use of modern Generative Adversarial Nets to generate novel
images of oil paintings using the Painter By Numbers dataset. We implemented
Spectral Normalization GAN (SN-GAN) and Spectral Normalization GAN with
Gradient Penalty, and compared their outputs to a Deep Convolutional GAN.
Visually, and quantitatively according to the Sliced Wasserstein Distance
metric, we determined that the SN-GAN produced paintings that were most
comparable to our training dataset. We then performed a series of experiments
to add supervised conditioning to SN-GAN, the culmination of which is what we
believe to be a novel architecture that can generate face paintings with
user-specified characteristics.
