Introduction to style-based GANs

The innovations in style transfer made their way into influencing the development of GANs. Although GANs at that time could generate realistic images, they were generated by using random latent variables, where we had little understanding in terms of what they represented. Even though multimodal GANs could create variations in generated images, we did not know how to control the latent variables to achieve the outcome that we wanted.

In an ideal world, we would love to have some knobs to independently control the features we would like to generate, as in the face manipulation exercise in Chapter 2, Variational Autoencoder. This is known as disentangled representation, which is a relatively new idea in deep learning. The idea of disentangled representation is to separate an image into independent representation. For example, a face has two eyes, a nose, and a mouth, with each of them being a representation of a face. As we have learned in style transfer...