In this paper, we propose an approach based on domain conditional normalization (DCN) for zero-pair image-to-image translation, i.e., translating between two domains which have no paired training data available but each have paired training data with a third domain. We employ a single generator which has an encoder-decoder structure and analyze different implementations of domain conditional normalization to obtain the desired target domain output. The validation benchmark uses RGB-depth pairs and RGB-semantic pairs for training and compares performance for the depth-semantic translation task. The proposed approaches improve in qualitative and quantitative terms over the compared methods, while using much fewer parameters. Code available at https://github.com/samarthshukla/dcn
@article{arxiv.2011.05680,
title = {Zero-Pair Image to Image Translation using Domain Conditional Normalization},
author = {Samarth Shukla and Andrés Romero and Luc Van Gool and Radu Timofte},
journal= {arXiv preprint arXiv:2011.05680},
year = {2020}
}