# Adversarial Pixel-Level Generation of Semantic Images Source: [Adversarial Pixel-Level Generation of Semantic Images]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:39:29.972Z Authors: Emanuele Ghelfi, Paolo Galeone, Michele De Simoni, Federico Di Mattia Published: 2019-06-27T14:25:11.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()