Style 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 an upper hand. Not only is art subjective and has no defined boundaries, 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 years, a few examples being Deep Dream1 and Neural Style Transfer.2
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 Figure 7.1, where the CycleGAN architecture has been used to successfully transform photographs into paintings using the styles of famous artists such as Monet and Van Gogh.
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