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

Controlling face attributes

Everything we have done in this chapter serves only one purpose: to prepare us for face editing! This is the climax of this chapter!

Latent space arithmetic

We have talked about the latent space several times now but haven't given it a proper definition. Essentially, it means every possible value of the latent variables. In our VAE, it is a vector of 200 dimensions, or simply 200 variables. As much as we hope each variable has a distinctive semantic meaning to us, such as z[0] is for eyes, z[1] dictates the eye color, and so on, things are never that straightforward. We will simply have to assume the information is encoded in all the latent vectors and we can use vector arithmetic to explore the space.

Before diving into high-dimensional space, let's try to understand it using a two-dimensional example. Imagine you are now at point (0,0) on a map and your home is at (x,y). Therefore, the direction toward your home is (x – 0 ,y...