  Semantic Image Synthesis (SIS) is a subclass of image-to-image translation
where a semantic layout is used to generate a photorealistic image.
State-of-the-art conditional Generative Adversarial Networks (GANs) need a huge
amount of paired data to accomplish this task while generic unpaired
image-to-image translation frameworks underperform in comparison, because they
color-code semantic layouts and learn correspondences in appearance instead of
semantic content. Starting from the assumption that a high quality generated
image should be segmented back to its semantic layout, we propose a new
Unsupervised paradigm for SIS (USIS) that makes use of a self-supervised
segmentation loss and whole image wavelet based discrimination. Furthermore, in
order to match the high-frequency distribution of real images, a novel
generator architecture in the wavelet domain is proposed. We test our
methodology on 3 challenging datasets and demonstrate its ability to bridge the
performance gap between paired and unpaired models.
