GANILLA: Generative adversarial networks for image to illustration translation Abstract   In this paper, we explore illustrations in childrenu0027s books as a new domain in unpaired imagetoimage translation. We show that although the current stateoftheart imagetoimage 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 welldefined or agreedupon evaluation metrics for unpaired imagetoimage 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 imagetoillustration 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 stateoftheart models on the illustrations dataset. Our code and pretrained models can be found at  https:github.comgiddyyuppganilla .

A StyleBased Generator Architecture for Generative Adversarial Networks We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of highlevel attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scalespecific control of the synthesis. The new generator improves the stateoftheart in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and highquality dataset of human faces.