Publication
Unsupervised Image-to-Image Translation: A Review
Henri Hoyez; Cedric Schockaert; Jason Raphael Rambach; Bruno Mirbach; Didier Stricker
In: Sensors - Open Access Journal (Sensors), Vol. 2022, 22, 8540, MDPI, 2022.
Abstract
Supervised image-to-image translation has been proven to generate realistic images with
sharp details and to have good quantitative performance. Such methods are trained on a paired
dataset, where an image from the source domain already has a corresponding translated image in
the target domain. However, this paired dataset requirement imposes a huge practical constraint,
requires domain knowledge or is even impossible to obtain in certain cases. Due to these problems,
unsupervised image-to-image translation has been proposed, which does not require domain expertise and can take advantage of a large unlabeled dataset.
Although such models perform well, they are hard to train due to the major constraints induced in their loss functions, which make training
unstable. Since CycleGAN has been released, numerous methods have been proposed which try to
address various problems from different perspectives. In this review, we firstly describe the general
image-to-image translation framework and discuss the datasets and metrics involved in the topic.
Furthermore, we revise the current state-of-the-art with a classification of existing works. This part is
followed by a small quantitative evaluation, for which results were taken from papers