Hands-On Generative Adversarial Networks with Keras

Whats next in GANs

Now that you have been deeply exposed to deep learning and Generative Adversarial Networks (GANs), in this chapter, you will learn about the possible future avenues for GANs! We start with a summary of this book, the topics that we covered, and the knowledge that we have gained so far.

Next, we address important open questions related to GANs that are essential for interacting with GAN models. We briefly pose questions related to how important architectures are, whether GANs really learn the target distribution, whether GANs are dependent on the inductive bias of architectures, and how to identify GAN samples.

Following this, we consider the artistic use of GANs in the visual and sonic arts. In the visual arts, we provide ...