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

Spectral normalization

Spectral normalization is an important method to stabilize GAN training and it has been used in a lot of recent state-of-the-art GANs. Unlike batch normalization or other normalization methods that normalize the activation, spectral normalization normalizes the weights instead. The aim of spectral normalization is to limit the growth of the weights, so the networks adhere to the 1-Lipschitz constraint. This has proved effective in stabilizing GAN training, as we learned in Chapter 3, Generative Adversarial Network.

We will revise WGANs to give us a better understanding of the idea behind spectral normalization. The WGAN discriminator (also known as the critic) needs to keep its prediction to small numbers to meet the 1-Lipschtiz constraint. WGANs do this by naively clipping the weights to the range of [-0.01, 0.01].

This is not a reliable method as we need to fine-tune the clipping range, which is a hyperparameter. It would be nice if there was a systematic...