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


We have definitely learned a lot in this chapter. We started by learning about the theory and loss functions of GANs, and how to translate the mathematical value function into the code implementation of binary cross-entropy loss. We implemented DCGAN with convolutional layers, batch normalization layers, and leaky ReLU to make the networks go deeper. However, there are still challenges in training GANs, which include instability and being prone to mode collapse due to Jensen-Shannon divergence.

Many of these problems were solved by WGAN with Wasserstein distance, weight clipping, and the removal of the sigmoid at the critic's output. Finally, WGAN-GP introduces gradient penalty to properly enforce the 1-Lipztschitz constraint and give us a framework for stable GAN training. We then replaced batch normalization with layer normalization to train on the CelebA dataset successfully to generate a good variety of faces.

This concludes part 1 of the book. Well done to...