GANILLA: Generative adversarial networks for image to illustration translation

Date

2020-03-01

Author

Hicsonmez, Samet
Samet, Nermin
Akbaş, Emre
DUYGULU ŞAHİN, PINAR

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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 sametime. 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.

Subject Keywords

Computer Vision and Pattern Recognition, Generative Adversarial Networks, Image to Image Translation, Illustrations, Style Transfer

URI

https://hdl.handle.net/11511/34821

Collections

Department of Computer Engineering, Article