Hands-On Image Generation with TensorFlow

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.

Type
ebook
Category
Data
publication date
2020-12-24
what you will learn

Train on face datasets and use them to explore latent spaces for editing new faces
Get to grips with swapping faces with deepfakes
Perform style transfer to convert a photo into a painting
Build and train pix2pix, CycleGAN, and BicycleGAN for image-to-image translation
Use iGAN to understand manifold interpolation and GauGAN to turn simple images into photorealistic images
Become well versed in attention generative models such as SAGAN and BigGAN
Generate high-resolution photos with Progressive GAN and StyleGAN

no of pages
306
duration
612
key features
Understand the different architectures for image generation, including autoencoders and GANs * Build models that can edit an image of your face, turn photos into paintings, and generate photorealistic images * Discover how you can build deep neural networks with advanced TensorFlow 2.x features
approach
This book will cover step-by-step explanations of essential concepts in detail using real-world practical examples. Each example will deep dive into architectures of various neural networks and GANs to generate images and videos.
audience
The Hands-On Image Generation with TensorFlow book is for deep learning engineers, practitioners, and researchers who have basic knowledge of convolutional neural networks and want to learn various image generation techniques using TensorFlow 2.x. You’ll also find this book useful if you are an image processing professional or computer vision engineer looking to explore state-of-the-art architectures to improve and enhance images and videos. Knowledge of Python and TensorFlow will help you to get the best out of this book.

meta description
Implement various state-of-the-art architectures, such as GANs and autoencoders, for image generation using TensorFlow 2.x from scratch
short description
This book is a step-by-step guide to show you how to implement generative models in TensorFlow 2.x from scratch. You’ll get to grips with the image generative technology by covering autoencoders, style transfer, and GANs as well as fundamental and state-of-the-art models.
subtitle
A practical guide to generating images and videos using deep learning
keywords
GAN technology, TensorFlow 2.0, Neural networks and deep learning, Image processing, GAN book, TensorFlow image processing, GAN networks, GAN neural network
Product ISBN
9781838826789