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-theart 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

Guided neural style transfer for shape stylization

“…However, the smaller shape elements must be prepared for manipulation beforehand. By using a CNN, Sage et al [18] used Generative Adversarial Networks (GAN) [19] to generate logos. They gathered various logos from the Internet to create the Large Logo Dataset (LLD) and trained a GAN with the LLD to synthesize new logos.…”

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

“…Up until the writing of this paper, logo generation has previously only been investigated by Sage et al [6], who accomplish three main things:…”

“…To the best of our knowledge, Sage, We gratefully acknowledge the support of Mediaan in this research especially for providing the necessary computing resources. et al [6] to be only one to have tackled this problem thus far. They propose a clustered approach for dealing with multimodal data, specifically logos.…”

Comparative Analysis of Edge Information and Polarization on SAR-to-Optical Translation Based on Conditional Generative Adversarial Networks

“…Therefore, conditional GANs (cGANs) were developed to deal with complex images [40], whereby additional information is used to condition the models and direct the data generation process of cGANs. cGANs have attracted considerable interest in the remote sensing community [41], as they allow to generate desired artificial data based on a specified target output and have achieved promising results in many fields, such as image inpainting [42][43][44], image manipulation [45][46][47], and image translation [48][49][50][51][52]. More specifically, cGANs can be employed to efficiently translate SAR images to optical images, and have been proved to be suitable in the SAR-to-optical translation process [6,16,17,20,[53][54][55][56][57][58].…”