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 8: Self-Attention for Image Generation

You may have heard about some popular Natural Language Processing (NLP) models, such as the Transformer, BERT, or GPT-3. They all have one thing in common – they all use an architecture known as a transformer that is made up of self-attention modules.

Self-attention is gaining widespread adoption in computer vision, including classification tasks, which makes it an important topic to master. As we will learn in this chapter, self-attention helps us to capture important features in the image without using deep layers for large effective receptive fields. StyleGAN is great for generating faces, but it will struggle to generate images from ImageNet.

In a way, faces are easy to generate, as eyes, noses, and lips all have similar shapes and are in similar positions across various faces. In contrast, the 1,000 classes of ImageNet contain varied objects (dogs, trucks, fish, and pillows, for instance) and backgrounds. Therefore...