  In this paper, we explore illustrations in children's books as a new domain
in unpaired image-to-image translation. We show that although the current
state-of-the-art image-to-image translation models successfully transfer either
the style or the content, they fail to transfer both at the same time. We
propose a new generator network to address this issue and show that the
resulting network strikes a better balance between style and content.
  There are no well-defined or agreed-upon evaluation metrics for unpaired
image-to-image translation. So far, the success of image translation models has
been based on subjective, qualitative visual comparison on a limited number of
images. To address this problem, we propose a new framework for the
quantitative evaluation of image-to-illustration models, where both content and
style are taken into account using separate classifiers. In this new evaluation
framework, our proposed model performs better than the current state-of-the-art
models on the illustrations dataset. Our code and pretrained models can be
found at https://github.com/giddyyupp/ganilla.
