Style Transfer with GANs
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. There have been several successful attempts at creating, understanding, and even copying art or artistic styles over the years1, 2. Generative models are well suited for 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 Figure 13.1, where the CycleGAN architecture has been used to successfully transform photographs into paintings using styles of famous artists such as Monet, Van Gogh, and so on.
Figure 13.1: Style transfer based on the artistic style of four famous painters using CycleGAN3
Figure 13.1 gives us a visual sense of how...
