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

Reviewing GANs

Apart from PixelCNN, which we covered in Chapter 1, Getting Started with Image Generation Using TensorFlow, which is a CNN, all the other generative models we have learned about are based on (variational) autoencoders or generative adversarial networks (GANs). Strictly speaking, a GAN is not a network but a training method that makes use of two networks – a generator and a discriminator. I tried to fit a lot of content into this book; so, the information can be overwhelming. We will now go over a summary of the important techniques we have learned, by grouping them into the following categories:

  • Optimizer and activation functions
  • Adversarial loss
  • Auxiliary loss
  • Normalization
  • Regularization

Optimizer and activation functions

Adam is the most popular optimizer in training GANs, followed by RMSprop. Typically, the first moment in Adam is set to 0 and the second moment is set to 0.999. The learning rate for the generator is set to...