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.

arXiv commentAccepted 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