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

Video synthesis overview

Let's say your doorbell rings while you're watching a video, so you pause the video and go to answer the door. What would you see on your screen when you come back? A still picture where everything is frozen and not moving. If you press the play button and pause it again quickly, you will see another image that looks very similar to the previous one but with slight differences. Yes – when you play a series of images sequentially, you get a video.

We say that image data has three dimensions, or (H, W, C); video data has four dimensions, (T, H, W, C), where T is the temporal (time) dimension. It's also the case that video is just a big batch of images, except that we cannot shuffle the batch. There must be temporal consistency between the images; I'll explain this further.

Let's say we extract images from some video datasets and train an unconditional GAN to generate images from random noise input. As you can imagine, the...