2018/02/13 by Mohan Nikam, Nikam, Mohan
Computer Science · #Digital Imaging for Blood Diseases #Generative Adversarial Networks and Image Synthesis #Advanced Image Processing Techniques
paper · pdf · doi:10.48550/arxiv.1802.04467
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.