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

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...