%0 Conference Proceedings
%T SDIT: Scalable and Diverse Cross-domain Image Translation
%A Yaxing Wang
%A Abel Gonzalez-Garcia
%A Joost Van de Weijer
%A Luis Herranz
%B 27th ACM International Conference on Multimedia
%D 2019
%F Yaxing Wang2019
%O LAMP; 600.106; 600.109; 600.141; 600.120
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3363), last updated on Tue, 08 Feb 2022 12:11:12 +0100
%X Recently, image-to-image translation research has witnessed remarkable progress. Although current approaches successfully generate diverse outputs or perform scalable image transfer, these properties have not been combined into a single method. To address this limitation, we propose SDIT: Scalable and Diverse image-to-image translation. These properties are combined into a single generator. The diversity is determined by a latent variable which is randomly sampled from a normal distribution. The scalability is obtained by conditioning the network on the domain attributes. Additionally, we also exploit an attention mechanism that permits the generator to focus on the domain-specific attribute. We empirically demonstrate the performance of the proposed method on face mapping and other datasets beyond faces.
%U https://doi.org/10.1145/3343031.3351004
%U http://refbase.cvc.uab.es/files/WGW2019.pdf
%P 1267–1276