One task that is often discussed in a computer vision is the mapping of an image from one domain to a corresponding image in another domain known as image-to-image translation. Currently there are several approaches solving this task. In this paper, we present an enhancement of the UNIT framework that aids in removing its main drawbacks. More specifically, we introduce an additional adversarial discriminator on the latent representation used instead of VAE, which enforces the latent space distributions of both domains to be similar. On MNIST and USPS domain adaptation tasks, this approach greatly outperforms competing approaches.
@article{arxiv.2003.09149,
title = {Unsupervised Latent Space Translation Network},
author = {Magda Friedjungová and Daniel Vašata and Tomáš Chobola and Marcel Jiřina},
journal= {arXiv preprint arXiv:2003.09149},
year = {2020}
}
Comments
To be published in conference proceedings of ESANN 2020