[2N1-IS-2a] Machine learning (1/5)

[2N1-IS-2a-01] Multi-CartoonGAN for Conditional Artistic Face Translation

〇Rina Komatsu¹, Tad Gonsalves¹ (1. Sophia University)

キーワード：Deep Learning, GAN, Image-to-Image Translation, Multi Modal Translation

This study deals with photographic face to conditional artistic face illustrations in the form of portrait, anime and emaki following the content of conditional input. Different from cityscape to segmentation task, face to illustration translation task requires large texture changing especially translation to anime face which includes characteristic edges and shapes. Related works try mapping between domains with a large number of varying features. However, incorporating more modules for adopting geometric change level translation learning and reusing Generators for keeping cycle consistency exorbitantly increases the computational cost of model training.
Our study aims to establish conditional translation model which has the potential to learn diverse and large feature mappings using only a small number of training parameters. We developed Multi-CartoonGAN employing central biasing normalization as conditional input and adaptive layer instance normalization to make translation learning robust. As can be seen from our translation learning and test demonstration, our model greatly reduces the computational cost of parameter training and performs conditional translation even when the target domain has features quite different from the real-world face.