Generative Adversarial Networks Projects

CycleGAN - Turn Paintings into Photos

CycleGAN is a type of Generative Adversarial Network (GAN) for cross-domain transfer tasks, such as changing the style of an image, turning paintings into photos, and vice versa, photo enhancement, changing the season of a photo, and many more. CycleGANs were introduced by Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros in a paper entitled: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. This was produced in February 2018 at the Berkeley AI Research (BAIR) laboratory, UC Berkeley, which is available at the following link: https://arxiv.org/pdf/1703.10593.pdf. CycleGANs caused a stir in the GAN community because of their widespread use cases. In this chapter, ...