Generative Adversarial Networks (GANs) have obtained extraordinary success in the generation of realistic images, a domain where a lower pixel-level accuracy is acceptable. We study the problem, not yet tackled in the literature, of generating semantic images starting from a prior distribution. Intuitively this problem can be approached using standard methods and architectures. However, a better-suited approach is needed to avoid generating blurry, hallucinated and thus unusable images since tasks like semantic segmentation require pixel-level exactness. In this work, we present a novel architecture for learning to generate pixel-level accurate semantic images, namely Semantic Generative Adversarial Networks (SemGANs). The experimental evaluation shows that our architecture outperforms standard ones from both a quantitative and a qualitative point of view in many semantic image generation tasks.
@article{arxiv.1906.12195,
title = {Adversarial Pixel-Level Generation of Semantic Images},
author = {Emanuele Ghelfi and Paolo Galeone and Michele De Simoni and Federico Di Mattia},
journal= {arXiv preprint arXiv:1906.12195},
year = {2019}
}