  Unsupervised style transfer that supports diverse input styles using only one
trained generator is a challenging and interesting task in computer vision.
This paper proposes a Multi-IlluStrator Style Generative Adversarial Network
(MISS GAN) that is a multi-style framework for unsupervised
image-to-illustration translation, which can generate styled yet content
preserving images. The illustrations dataset is a challenging one since it is
comprised of illustrations of seven different illustrators, hence contains
diverse styles. Existing methods require to train several generators (as the
number of illustrators) to handle the different illustrators' styles, which
limits their practical usage, or require to train an image specific network,
which ignores the style information provided in other images of the
illustrator. MISS GAN is both input image specific and uses the information of
other images using only one trained model.
