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 learned about an important network architecture known as self-attention. The effectiveness of the convolutional layer is limited by its receptive field, and self-attention helps to capture important features including activations that are spatially-distanced from conventional convolutional layers. We have learned how to write a custom layer to insert into a SAGAN. The SAGAN is a state-of-the-art class-conditional GAN. We also implemented conditional batch normalization to learn different learnable parameters specific to each class. Finally, we looked at the bulked-up version of the SAGAN known as the BigGAN, which trumps SAGAN's performance significantly in terms of both image resolution and class variation.

We have now learned about most, if not all, of the important GANs for image generation. In recent years, two major components have gained popularity in the GAN world – they are AdaIN for the StyleGAN as covered in Chapter 7, High Fidelity...