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In this paper we introduce Local Logo Generative Adversarial Network (LL-GAN)that uses regional features extracted from Faster R-CNN for logo generation. Wedemonstrate the strength of this approach by training the framework on a smallstyle-rich dataset of real heavy metal logos to generate new ones. LL-GANachieves Inception Score of 5.29 and Frechet Inception Distance of 223.94,improving on state-of-the-art models StyleGAN2 and Self-Attention GAN.

Logo Generation Using Regional Features: A Faster R-CNN Approach to Generative Adversarial Networks