  We propose a new neural network architecture for solving single-image
analogies - the generation of an entire set of stylistically similar images
from just a single input image. Solving this problem requires separating image
style from content. Our network is a modified variational autoencoder (VAE)
that supports supervised training of single-image analogies and in-network
evaluation of outputs with a structured similarity objective that captures
pixel covariances. On the challenging task of generating a 62-letter font from
a single example letter we produce images with 22.4% lower dissimilarity to the
ground truth than state-of-the-art.
