# LoGAN: Generating Logos with a Generative Adversarial Neural Network Conditioned on color

arXiv: https://arxiv.org/abs/1810.10395 · HTML: https://mlsanity.com/paper/1810.10395/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2018.

## Built on by (most cited first, 6 of 6)

- [A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing](https://mlsanity.com/paper/2110.01442/) (2021)
- [Pursuit of a Discriminative Representation for Multiple Subspaces via Sequential Games](https://mlsanity.com/paper/2206.09120/) (2022)
- [Design What You Desire: Icon Generation from Orthogonal Application and Theme Labels](https://mlsanity.com/paper/2208.00439/) (2022)
- [Logo Generation Using Regional Features: A Faster R-CNN Approach to Generative Adversarial Networks](https://mlsanity.com/paper/2109.12628/) (2021)
- [Learning Geometric Invariant Features for Classification of Vector Polygons with Graph Message-passing Neural Network](https://mlsanity.com/paper/2407.04334/) (2024)
- [Scaling Federated Learning Solutions with Kubernetes for Synthesizing Histopathology Images](https://mlsanity.com/paper/2504.04130/) (2025)

## Abstract (arXiv)

Designing a logo is a long, complicated, and expensive process for any designer. However, recent advancements in generative algorithms provide models that could offer a possible solution. Logos are multi-modal, have very few categorical properties, and do not have a continuous latent space. Yet, conditional generative adversarial networks can be used to generate logos that could help designers in their creative process. We propose LoGAN: an improved auxiliary classifier Wasserstein generative adversarial neural network (with gradient penalty) that is able to generate logos conditioned on twelve different colors. In 768 generated instances (12 classes and 64 logos per class), when looking at the most prominent color, the conditional generation part of the model has an overall precision and recall of 0.8 and 0.7 respectively. LoGAN's results offer a first glance at how artificial intelligence can be used to assist designers in their creative process and open promising future directions, such as including more descriptive labels which will provide a more exhaustive and easy-to-use system.