Fundamentals and Challenges of Generative Adversarial Networks for Image-based Applications

• Vinicius Luis Trevisan De Souza UFABC
• Bruno Augusto Dorta Marques UFABC
• João Paulo Gois UFABC

Resumo

Significant advances in image-based applications have been achieved in recent years, many of which are arguably due to recent developments in Generative Adversarial Networks (GANs). Although the continuous improvement in the architectures of GAN has significantly increased the quality of synthetic images, this is not without challenges such as training stability and convergence issues, to name a few. In this work, we present the fundamentals and notable architectures of GANs, especially for image-based applications. We also discuss relevant issues such as training problems, diversity generation, and quality assessment (metrics).

Palavras-chave: Training, Measurement, Graphics, Neural networks, Generative adversarial networks, Quality assessment, Convergence, Generative Adversarial Network, image manipulation, deep image synthesis, deep neural network