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

Chapter 5: Style Transfer

Generative models such as VAE and GAN are great at generating realistic looking images. But we understand very little about the latent variables, let alone how to control them with regard to image generation. Researchers began to explore ways to better represent images aside from pixel distribution. It was found that an image could be disentangled into content and style. Content describes the composition in the image such as a tall building in the middle of the image. On the other hand, style refers to the fine details, such as the brick or stone textures of the wall or the color of the roof. Images showing the same building at different times of the day have different hues and brightness and can be seen as having the same content but different styles.

In this chapter, we will start by implementing some seminal work in neural style transfer to transfer the artistic style of an image. We will then learn to implement feed-forward neural style transfer, which...