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

26 Sep 2021 · Aram Ter-Sarkisov, Eduardo Alonso ·

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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Generative Adversarial Network

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Convolution • Faster R-CNN • Leaky ReLU • Path Length Regularization • R1 Regularization • RoIPool • RPN • Softmax • StyleGAN2 • Weight Demodulation

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

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