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

Summary

In this chapter, we covered the evolution of styled-based generative models. It all started with neural style transfer, where we learned that the image can be disentangled into content and style. The original algorithm was slowed and the iterative optimization process in inference time replaced with a feed-forward style transfer that could perform style transfer in real time.

We then learned that the Gram matrix is not the only method for representing style, and that we could use the layers' statistics instead. As a result, normalization layers have been explored to control the style of an image, which eventually led to the creation of AdaIN. By combing a feed-forward network and AdaIN, we implemented arbitrary style transfer in real time.

With the success in style transfer, AdaIN found its way into GANs. We went over the MUNIT architecture in detail in terms of how AdaIN was used for multimodal image generation. There is a style-based GAN that you should be familiar...