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

Diversifying translation with BicyleGAN

Both Pix2pix and CycleGAN came from the Berkeley AI Research (BAIR) laboratory at UC Berkeley. They are popular and have a number of tutorials and blogs about them online, including on the official TensorFlow site. BicycleGAN is what I see as the last of the image-to-image translation trilogy from that research group. However, you don't find a lot of example code online, perhaps due to its complexity.

In order to build the most advanced network in this book up to this point, we will throw in all the knowledge you have acquired in this chapter, plus the last two chapters. Maybe that is why it is regarded as advanced by many. Don't worry; you already have all the prerequisite knowledge. Let's jump in!

Understanding architecture

Before jumping straight into implementation, let me give you an overview of BicycleGAN. From the name, you may naturally think that BicycleGAN is an upgrade of CycleGAN by adding another cycle (from...