Hands-On Image Generation with TensorFlow: A practical guide to generating images and videos using deep learning

Cheong, Soon Yau

  • 出版商: Packt Publishing
  • 出版日期: 2020-12-24
  • 售價: $2,130
  • 貴賓價: 9.5$2,024
  • 語言: 英文
  • 頁數: 306
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1838826785
  • ISBN-13: 9781838826789
  • 相關分類: DeepLearningTensorFlow
  • 海外代購書籍(需單獨結帳)

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商品描述

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 (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.


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


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.

商品描述(中文翻譯)

實現各種最先進的架構,如GAN和自編碼器,使用TensorFlow 2.x從頭開始進行圖像生成

主要特點:
- 瞭解圖像生成的不同架構,包括自編碼器和GAN
- 構建可以編輯您的臉部圖像、將照片轉換為繪畫和生成逼真圖像的模型
- 發現如何使用先進的TensorFlow 2.x功能構建深度神經網絡

書籍描述:
生成對抗網絡(GAN)這一新興領域使得從現有數據集生成無法區分的圖像成為可能。通過這本實踐性的書籍,您不僅將開發圖像生成技能,還將對底層原理有深入的理解。

從介紹使用TensorFlow進行圖像生成的基礎知識開始,本書涵蓋了變分自編碼器(VAEs)和GANs。您將學習如何構建不同應用的模型,例如使用deepfakes進行臉部交換、神經風格轉移、圖像到圖像的轉換、將簡單圖像轉換為逼真圖像等等。在使用先進技術(如頻譜正則化和自注意層)構建最先進的深度神經網絡之前,您還將了解如何以及為什麼這樣做。您還將介紹照片修復、文本到圖像合成、視頻重定向和神經渲染。在整本書中,您將學習如何在TensorFlow 2.x中從頭開始實現模型,包括PixelCNN、VAE、DCGAN、WGAN、pix2pix、CycleGAN、StyleGAN、GauGAN和BigGAN。

通過閱讀本書,您將熟練掌握TensorFlow,並能夠自信地實現圖像生成技術。

您將學到:
- 使用人臉數據集進行訓練,並使用它們來探索編輯新面孔的潛在空間
- 熟悉使用deepfakes進行臉部交換
- 執行風格轉移,將照片轉換為繪畫
- 構建和訓練pix2pix、CycleGAN和BicycleGAN進行圖像到圖像的轉換
- 使用iGAN了解流形插值,使用GauGAN將簡單圖像轉換為逼真圖像
- 熟悉注意力生成模型,如SAGAN和BigGAN
- 使用Progressive GAN和StyleGAN生成高分辨率照片

本書適合深度學習工程師、從業人員和研究人員,他們具備卷積神經網絡的基礎知識,並希望學習使用TensorFlow 2.x進行各種圖像生成技術。如果您是圖像處理專業人員或計算機視覺工程師,並希望探索最先進的架構以改進和增強圖像和視頻,本書也將對您有所幫助。熟悉Python和TensorFlow將有助於您充分利用本書的內容。