An Optimized Architecture for Unpaired Image-to-Image Translation
Abstract
Unpaired Image-to-Image translation aims to convert the image from one domain (input domain A) to another domain (target domain B), without providing paired examples for the training. The state-of-the-art, Cycle-GAN demonstrated the power of Generative Adversarial Networks with Cycle-Consistency Loss. While its results are promising, there is scope for optimization in the training process. This paper introduces a new neural network architecture, which only learns the translation from domain A to B and eliminates the need for reverse mapping (B to A), by introducing a new Deviation-loss term. Furthermore, few other improvements to the Cycle-GAN are found and utilized in this new architecture, contributing to significantly lesser training duration.
Cite
@article{arxiv.1802.04467,
title = {An Optimized Architecture for Unpaired Image-to-Image Translation},
author = {Mohan Nikam},
journal= {arXiv preprint arXiv:1802.04467},
year = {2018}
}
Comments
Accepted to be published in Springer Advances in Intelligent Systems and Computing (AISC) Series 11156. Accepted for presentation in Springer ICANI (International Conference on Advanced computing, Networking and Informatics)-2018