@InProceedings{YaxingWang2019,
author="Yaxing Wang
and Abel Gonzalez-Garcia
and Joost Van de Weijer
and Luis Herranz",
title="SDIT: Scalable and Diverse Cross-domain Image Translation",
booktitle="27th ACM International Conference on Multimedia",
year="2019",
pages="1267--1276",
abstract="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.",
optnote="LAMP; 600.106; 600.109; 600.141; 600.120",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3363), last updated on Tue, 08 Feb 2022 12:11:12 +0100",
opturl="https://doi.org/10.1145/3343031.3351004",
file=":http://refbase.cvc.uab.es/files/WGW2019.pdf:PDF"
}