Research Article
Logo Generation Using Regional Features: A Faster R-CNN Approach to Generative Adversarial Networks
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@INPROCEEDINGS{10.1007/978-3-030-95531-1_30, author={Aram Ter-Sarkisov and Eduardo Alonso}, title={Logo Generation Using Regional Features: A Faster R-CNN Approach to Generative Adversarial Networks}, proceedings={ArtsIT, Interactivity and Game Creation. Creative Heritage. New Perspectives from Media Arts and Artificial Intelligence. 10th EAI International Conference, ArtsIT 2021, Virtual Event, December 2-3, 2021, Proceedings}, proceedings_a={ARTSIT}, year={2022}, month={2}, keywords={Deep learning Generative adversarial networks Logo generation.}, doi={10.1007/978-3-030-95531-1_30} }- Aram Ter-Sarkisov
Eduardo Alonso
Year: 2022
Logo Generation Using Regional Features: A Faster R-CNN Approach to Generative Adversarial Networks
ARTSIT
Springer
DOI: 10.1007/978-3-030-95531-1_30
Abstract
In this paper we introduce Local Logo Generative Adversarial Network (LL-GAN) that uses regional features extracted from Faster R-CNN for logo generation. We demonstrate the strength of this approach by training the framework on a small style-rich dataset of real heavy metal logos to generate new ones. LL-GAN achieves Inception Score of 5.29 and Frechet Inception Distance of 223.94, improving on state-of-the-art models StyleGAN2 and Self-Attention GAN .
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