# Adversarial Pixel-Level Generation of Semantic Images

arXiv: https://arxiv.org/abs/1906.12195 · HTML: https://mlsanity.com/paper/1906.12195/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2019.

## Built on by (most cited first, 1 of 1)

- [Generation of Synthetic Images for Pedestrian Detection Using a Sequence of GANs](https://mlsanity.com/paper/2401.07370/) (2024)

## Abstract (arXiv)

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.