On Unsupervised Image-to-image translation and GAN stability
BahaaEddin AlAila Zahra Jandaghi Abolfazl Farahani Mohammad Ziad Al-Saad
The problem of image-to-image translation is one that is intruiging and
challenging at the same time, for the impact potential it can have on a wide
variety of other computer vision applications like colorization, inpainting,
segmentation and others. Given the high-level of sophistication needed to
extract patterns from one domain and successfully applying them to another,
especially, in a completely unsupervised (unpaired) manner, this problem has
gained much attention as of the last few years. It is one of the first problems
where successful applications to deep generative models, and especially
Generative Adversarial Networks achieved astounding results that are actually
of realworld impact, rather than just a show of theoretical prowess; the such
that has been dominating the GAN world. In this work, we study some of the
failure cases of a seminal work in the field, CycleGAN [1] and hypothesize that
they are GAN-stability related, and propose two general models to try to
alleviate these problems. We also reach the same conclusion of the problem
being ill-posed that has been also circulating in the literature lately.