What this book covers
Chapter 1, Getting Started with Image Generation Using TensorFlow, walks through the basics of pixel probability and uses it to build our first model to generate handwritten digits.
Chapter 2, Variational Autoencoder, explains how to build a variational autoencoder (VAE) and use it to generate and edit faces.
Chapter 3, Generative Adversarial Network, introduces the fundamentals of GANs and builds a DCGAN to generate photorealistic images. We'll then learn about new adversarial loss to stabilize the training.
Chapter 4, Image-to-Image Translation, covers a lot of models and interesting applications. We will first implement pix2pix to convert sketches to photorealistic photos. Then we'll use CycleGAN to transform a horse to a zebra. Lastly, we will use BicycleGAN to generate a variety of shoes.
Chapter 5, Style Transfer, explains how to extract the style from a painting and transfer it into a photo. We'll also learn advanced techniques to make neural style transfer run faster in runtime, and to use it in state-of-the-art GANs.
Chapter 6, AI Painter, goes through the underlying principles of image editing and transformation using interactive GAN (iGAN) as an example. Then we will build a GauGAN to create photorealistic building facades from a simple segmentation map.
Chapter 7, High Fidelity Face Generation, shows how to build a StyleGAN using techniques from style transfer. However, before that, we will learn to grow the network layer progressively using a Progressive GAN.
Chapter 8, Self-Attention for Image Generation, shows how to build self-attention into a Self-Attention GAN (SAGAN) and a BigGAN for conditional image generation.
Chapter 9, Video Synthesis, demonstrates how to use autoencoders to create a deepfake video. Along the way, we'll learn how to use OpenCV and dlib for face processing.
Chapter 10, Road Ahead, reviews and summarizes the generative techniques we have learned. Then we will look at how they are used as the basis of up-and-coming applications, including text-to-image-synthesis, video compression, and video retargeting.