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)
Section 1: Fundamentals of Image Generation with TensorFlow
Section 2: Applications of Deep Generative Models
Section 3: Advanced Deep Generative Techniques

Chapter 2: Variational Autoencoder

In the previous chapter, we looked at how a computer sees an image as pixels, and we devised a probabilistic model for pixel distribution for image generation. However, this is not the most efficient way to generate an image. Instead of scanning an image pixel by pixel, we first look at the image and try to understand what is inside. For example, a girl is sitting, wearing a hat, and smiling. Then we use that information to draw a portrait. This is how autoencoders work.

In this chapter, we will first learn how to use an autoencoder to encode pixels into latent variables that we can sample from to generate images. Then we will learn how to tweak it to create a more powerful model known as a variational autoencoder (VAE). Finally, we will train our VAE to generate faces and perform face editing. The following topics will be covered in this chapter:

  • Learning latent variables with autoencoders
  • Variational autoencoders
  • Generating faces...