Introduction: Syle Transfer with GANs

Neural networks are improving in a number of tasks involving analytical and linguistic skills. Creativity is one sphere where humans have had the upper hand. Not only is art subjective and has no defined boundaries, but it is also difficult to quantify. Yet, this has not stopped researchers from exploring the creative capabilities of algorithms. Over the years, there have been several successful attempts at creating, understanding, and even copying art or artistic styles, a few examples being DeepDreamMordvintsev, A., McDonald, K., Rudolph, L., Kim, J-S., Li, J., & daviga404. (2015). deepdream. GitHub repository. https://github.com/google/deepdream and Neural Style TransferGatys, L.A., Ecker, A.S., & Bethge, M. (2015). A Neural Algorithm of Artistic Style. arXiv. https://arxiv.org/abs/1508.06576.

Generative models are well suited to tasks associated with imagining and creating. Generative adversarial networks (GANs), in particular, have been studied and explored in detail for the task of style transfer over the years. One such example is presented in the figure below, where the CycleGAN architecture has been used to successfully transform photographs into paintings using the styles of famous artists such as Monet and Van GoghZhu, J-Y., Park, T., Isola, P., & Efros, A.A. (2017). Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. arXiv. https://arxiv. org/abs/1703.10593.