Chapter 5: Style Transfer
Generative models such as VAE and GAN are great at generating realistic looking images. But we understand very little about the latent variables, let alone how to control them with regard to image generation. Researchers began to explore ways to better represent images aside from pixel distribution. It was found that an image could be disentangled into content and style. Content describes the composition in the image such as a tall building in the middle of the image. On the other hand, style refers to the fine details, such as the brick or stone textures of the wall or the color of the roof. Images showing the same building at different times of the day have different hues and brightness and can be seen as having the same content but different styles.
In this chapter, we will start by implementing some seminal work in neural style transfer to transfer the artistic style of an image. We will then learn to implement feed-forward neural style transfer, which...
