Book Image

Hands-On Image Generation with TensorFlow

By : Soon Yau Cheong
Book Image

Hands-On Image Generation with TensorFlow

By: Soon Yau Cheong

Overview of this book

The emerging field of Generative Adversarial Networks (GANs) has made it possible to generate indistinguishable images from existing datasets. With this hands-on book, you’ll not only develop image generation skills but also gain a solid understanding of the underlying principles. Starting with an introduction to the fundamentals of image generation using TensorFlow, this book covers Variational Autoencoders (VAEs) and GANs. You’ll discover how to build models for different applications as you get to grips with performing face swaps using deepfakes, neural style transfer, image-to-image translation, turning simple images into photorealistic images, and much more. You’ll also understand how and why to construct state-of-the-art deep neural networks using advanced techniques such as spectral normalization and self-attention layer before working with advanced models for face generation and editing. You'll also be introduced to photo restoration, text-to-image synthesis, video retargeting, and neural rendering. Throughout the book, you’ll learn to implement models from scratch in TensorFlow 2.x, including PixelCNN, VAE, DCGAN, WGAN, pix2pix, CycleGAN, StyleGAN, GauGAN, and BigGAN. By the end of this book, you'll be well versed in TensorFlow and be able to implement image generative technologies confidently.
Table of Contents (15 chapters)
1
Section 1: Fundamentals of Image Generation with TensorFlow
5
Section 2: Applications of Deep Generative Models
9
Section 3: Advanced Deep Generative Techniques

Summary

Since the inception of GANs and VAEs in 2014, significant advancement has been made in 2D image generation. Generating high-fidelity images is still challenging in practice as it requires huge amounts of data, computing power, and hyperparameter tuning. However, as demonstrated by StyleGAN, it seems that we now have the technology to do this, especially in face generation.

In fact, at the time of writing this book, there haven't really been any major breakthroughs in this area since 2018. With this book, we have included all the important techniques leading to BigGAN. These techniques include the use of AdaIN and self-attention modules, which are now commonplace even in adjacent fields such as video synthesis. This gives us a solid foundation to explore other emerging generative technologies.

In this chapter, we looked back at the things we have learned and summarized them in different groups, such as losses and normalization techniques. We then looked at some...