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

Variational autoencoders

In an autoencoder, the decoder samples directly from latent variables. Variational autoencoders (VAEs), which were invented in 2014, differ in that the sampling is taken from a distribution parameterized by the latent variables. To be clear, let's say we have an autoencoder with two latent variables, and we draw samples randomly and get two samples of 0.4 and 1.2. We then send them to the decoder to generate an image.

In a VAE, these samples don't go to the decoder directly. Instead, they are used as a mean and variance of a Gaussian distribution, and we draw samples from this distribution to be sent to the decoder for image generation. As this is one of the most important distributions in machine learning, so let's go over some basics of Gaussian distributions before creating a VAE.

Gaussian distribution

A Gaussian distribution is characterized by two parameters – mean and variance. I think we are all familiar with the different...