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.

本文研究了儿童图书插图作为无配对图像转换的新领域，并提出了一种新的生成器网络来解决当前图像转换模型在样式和内容之间无法取得平衡的问题。同时，本文还提出了一种新的评估框架，通过单独的分类器来考虑内容和样式，并在插图数据集上表现更好。

GANILLA：用于图像到插图转换的生成对抗网络