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

GANILLA: Generative Adversarial Networks for Image to Illustration Translation