Hands-On Image Generation with TensorFlow

Hands-On Image Generation with TensorFlow
PDF, ePUB
  • eBook:
    Hands-On Image Generation with TensorFlow: A pragmatic guide to generating images and videos using deep learning
  • Author:
    Soon Yau Cheong
  • Edition:
    -
  • Categories:
  • Data:
    January 11, 2021
  • ISBN:
    1838826785
  • ISBN-13:
    9781838826789
  • Language:
    English
  • Pages:
    267 pages
  • Format:
    PDF, ePUB

Book Description
Implement various state-of-the-art architectures, such as GANs and autoencoders, for image generation using TensorFlow 2.x from scratch

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

Book Description

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 and GANs. You'll discover how to build models for different applications as you get to grips with performing face swap 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 photo restoration, face generation and editing, 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, and Progressive GAN.
By the end of this book, you'll be well-versed in TensorFlow and be able to implement image generative technologies confidently.

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 with attention generative models such as SAGAN and BigGAN
  • Generate high-resolution photos with Progressive GAN and StyleGAN

Who This Book Is For

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.

Content

Section 1: Fundamentals of Image Generation with TensorFlow
Chapter 1: Getting Started with Image Generation Using TensorFlow
Chapter 2: Variational Autoencoder
Chapter 3: Generative Adversarial Network

Section 2: Applications of Deep Generative Models
Chapter 4: Image-to-Image Translation
Chapter 5: Style Transfer
Chapter 6: AI Painter

Section 3: Advanced Deep Generative Techniques
Chapter 7: High Fidelity Face Generation
Chapter 8: Self-Attention for Image Generation
Chapter 9: Video Synthesis
Chapter 10: Road Ahead

Download Hands-On Image Generation with TensorFlow: A pragmatic guide to generating images and videos using deep learning PDF or ePUB format free


Free sample

Download in .PDF format



Download in .ePUB format


Add comments
Прокомментировать
Введите код с картинки:*
reload, if the code cannot be seen
Copyright © 2019