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

Building a Wasserstein GAN

Many have attempted to solve the instability of GAN training by using heuristic approaches such as trying different network architectures, hyperparameters, and optimizers. One major breakthrough happened in 2016 with the introduction of Wasserstein GAN (WGAN).

WGAN alleviates or even eliminates many of the GAN challenges we've discussed altogether. It no longer requires careful design of network architecture nor careful balancing of the discriminator and the generator. The mode collapse problem is also reduced drastically.  

The biggest fundamental improvement from the original GAN is the change of the loss function. The theory is that if the two distributions are disjointed, JSD will no longer be continuous, hence not differentiable, resulting in a zero gradient. WGAN solves this by using a new loss function that is continuous and differentiable everywhere!

The notebook for this exercise is ch3_wgan_fashion_mnist.ipynb.