Logo Synthesis and Manipulation with Clustered Generative Adversarial Networks

TL;DR

This paper introduces a clustering-based GAN approach for diverse and stable logo synthesis, enabling interactive logo design and demonstrating state-of-the-art results on CIFAR 10.

Contribution

Proposes a novel clustered GAN training method using synthetic labels to improve logo generation stability and diversity, validated on a large logo dataset and CIFAR 10.

Findings

High diversity of generated logos

Stable GAN training with synthetic labels

State-of-the-art Inception scores on CIFAR 10

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 https://data.vision.ee.ethz.ch/cvl/lld/.