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

Building a SAGAN

The SAGAN has a simple architecture that looks like DCGAN's. However, it is a class-conditional GAN that uses class labels to both generate and discriminate between images. In the following figure, each image on each row is generated from different class labels:

Figure 8.3 – Images generated by a SAGAN by using different class labels. (Source: A. Brock et al., 2018, "Large Scale GAN Training for High Fidelity Natural Image Synthesis,"

In this example, we will use the CIFAR10 dataset, which contains 10 classes of images with a resolution of 32x32. We will deal with the conditioning part later. Now, let's first complete the simplest part – the generator.

Building a SAGAN generator

At a high level, the SAGAN generator doesn't look very different from other GAN generators: it takes noise as input and goes through a dense layer, followed by multiple levels of upsampling...