Summary

This chapter explored one of the most exciting deep neural networks of our times: GANs. Unlike discriminative networks, GANs have the ability to generate images based on the probability distribution of the input space. We started with the first GAN model proposed by Ian Goodfellow and used it to generate handwritten digits. We next moved to DCGANs where convolutional neural networks were used to generate images and we saw the remarkable pictures of celebrities, bedrooms, and even album artwork generated by DCGANs. Finally, the chapter delved into some awesome GAN architectures: the SRGAN, CycleGAN, InfoGAN, and StyleGAN. The chapter also included an implementation of the CycleGAN in TensorFlow 2.0.

In this chapter and the ones before it, we have been continuing with different unsupervised learning models, with both autoencoders and GANs examples of self-supervised learning; the next chapter will further detail the difference between self-supervised, joint, and contrastive...