  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.
