Summary

This chapter explored one of the most exciting deep neural networks of our times: GANs. Unlike discriminative networks, GANs have an 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, and InfoGAN. The chapter also included an implementation of the CycleGAN in TensorFlow 2.0.

In this chapter and the ones before it we have been largely concerned with images; the next chapter will move into textual data. You will learn about word embeddings and learn to use some of the recent pretrained language models for embeddings.