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


In this chapter, we entered the realm of high-definition image generation with ProGAN. ProGAN first trains on low-resolution images before moving on to higher-resolution images. The network training becomes more stable by growing the network progressively. This lays the foundation for high-fidelity image generation, as this coarse-to-fine training method is adopted by other GANs. For example, pix2pixHD has two generators at two different scales, where the coarse generator is pre-trained before both are trained together. We have also learned about equalized learning rates, minibatch statistics, and pixel normalization, which are also used in StyleGAN.

With the use of the AdaIN layer from style transfer in the generator, not only does StyleGAN produce better-quality images, but this also allows control of features when mixing styles. By injecting different style code and noise at different scales, we can control both the global and fine details of an image. StyleGAN achieved...