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

Challenges in training GANs

GANs are notoriously difficult to train. We'll discuss some of the main challenges in training a GAN.

Uninformative loss and metrics

When training a CNN for classification or detection tasks, we can look at the shape of the loss plots to tell whether the network has converged or is overfitting and we'll know when to stop training. Then the metrics will correlate with the loss. For example, classification accuracy is normally the highest when the loss is the lowest. However, we can't do the same with GAN loss, as it doesn't have a minimum but fluctuates around some constant values after training for a while. We also could not correlate the generated image quality with the loss. A few metrics were invented to address this in the early days of GANs and one of them is the inception score.

A classification CNN known as inception is used to predict the confidence score of an image belonging to one of 1,000 categories in the ImageNet...