Title: Logo Synthesis and Manipulation with Clustered Generative Adversarial Networks,
Abstract: Designing a logo for a new brand is a lengthy and tedious back-and-forth
process between a designer and a client. In this paper we explore to what
extent machine learning can solve the creative task of the designer. For this,
we build a dataset -- LLD -- of 600k+ logos crawled from the world wide web.
Training Generative Adversarial Networks (GANs) for logo synthesis on such
multi-modal data is not straightforward and results in mode collapse for some
state-of-the-art methods. We propose the use of synthetic labels obtained
through clustering to disentangle and stabilize GAN training. We are able to
generate a high diversity of plausible logos and we demonstrate latent space
exploration techniques to ease the logo design task in an interactive manner.
Moreover, we validate the proposed clustered GAN training on CIFAR 10,
achieving state-of-the-art Inception scores when using synthetic labels
obtained via clustering the features of an ImageNet classifier. GANs can cope
with multi-modal data by means of synthetic labels achieved through clustering,
and our results show the creative potential of such techniques for logo
synthesis and manipulation. Our dataset and models will be made publicly
available at this https URL.