LoGAN: Generating Logos with a Generative Adversarial Neural Network Conditioned on color
This work addresses the time-consuming and costly process of logo design for designers, offering an AI-assisted tool, though it is incremental in applying existing methods to a new domain.
The paper tackled the problem of logo design by proposing LoGAN, a conditional generative adversarial network that generates logos based on twelve colors, achieving an overall precision of 0.8 and recall of 0.7 in color-conditioned generation.
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.