Generative Adversarial Networks Projects: Build next-generation generative models using TensorFlow and Keras

Kailash Ahirwar

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商品描述

Explore various Generative Adversarial Network architectures using the Python ecosystem

Key Features

  • Use different datasets to build advanced projects in the Generative Adversarial Network domain
  • Implement projects ranging from generating 3D shapes to a face aging application
  • Explore the power of GANs to contribute in open source research and projects

Book Description

Generative Adversarial Networks (GANs) have the potential to build next-generation models, as they can mimic any distribution of data. Major research and development work is being undertaken in this field since it is one of the rapidly growing areas of machine learning. This book will test unsupervised techniques for training neural networks as you build seven end-to-end projects in the GAN domain.

Generative Adversarial Network Projects begins by covering the concepts, tools, and libraries that you will use to build efficient projects. You will also use a variety of datasets for the different projects covered in the book. The level of complexity of the operations required increases with every chapter, helping you get to grips with using GANs. You will cover popular approaches such as 3D-GAN, DCGAN, StackGAN, and CycleGAN, and you'll gain an understanding of the architecture and functioning of generative models through their practical implementation.

By the end of this book, you will be ready to build, train, and optimize your own end-to-end GAN models at work or in your own projects.

What you will learn

  • Train a network on the 3D ShapeNet dataset to generate realistic shapes
  • Generate anime characters using the Keras implementation of DCGAN
  • Implement an SRGAN network to generate high-resolution images
  • Train Age-cGAN on Wiki-Cropped images to improve face verification
  • Use Conditional GANs for image-to-image translation
  • Understand the generator and discriminator implementations of StackGAN in Keras

Who this book is for

If you're a data scientist, machine learning developer, deep learning practitioner, or AI enthusiast looking for a project guide to test your knowledge and expertise in building real-world GANs models, this book is for you.

Table of Contents

  1. Introduction to Generative Adversarial Networks
  2. 3D-GAN - Generating Shapes Using GANs
  3. Face Aging Using Conditional GAN
  4. Generating Anime Characters Using DCGANs
  5. Using SRGANs to Generate Photo-Realistic Images
  6. StackGAN- Text to Photo-Realistic Image Synthesis
  7. CycleGAN- Turn Paintings into Photos
  8. Conditional GAN - Image-to-Image Translation Using Conditional Adversarial Networks
  9. Predicting the Future of GANs

商品描述(中文翻譯)

使用Python生態系統探索各種生成對抗網絡架構

主要特點



  • 使用不同的數據集在生成對抗網絡領域中構建高級項目

  • 實施從生成3D形狀到人臉老化應用的項目

  • 探索生成對抗網絡在開源研究和項目中的潛力

書籍描述


生成對抗網絡(GAN)具有構建下一代模型的潛力,因為它們可以模仿任何數據分佈。由於這是機器學習領域中快速增長的領域之一,因此在這個領域進行了大量的研究和開發工作。本書將在GAN領域中構建七個端到端項目,測試無監督技術來訓練神經網絡。

《生成對抗網絡項目》首先介紹了您將用於構建高效項目的概念、工具和庫。您還將使用各種數據集來進行書中涵蓋的不同項目。所需操作的複雜性隨著每個章節的增加而增加,幫助您掌握使用GAN的技巧。您將涵蓋流行的方法,如3D-GAN、DCGAN、StackGAN和CycleGAN,並通過實際實施了解生成模型的架構和功能。

通過閱讀本書,您將準備好在工作中或自己的項目中構建、訓練和優化自己的端到端GAN模型。

您將學到什麼



  • 使用3D ShapeNet數據集訓練網絡以生成逼真的形狀

  • 使用Keras實現DCGAN生成動漫角色

  • 實施SRGAN網絡以生成高分辨率圖像

  • 使用Wiki-Cropped圖像訓練Age-cGAN以改進人臉驗證

  • 使用條件GAN進行圖像到圖像的轉換

  • 了解Keras中StackGAN的生成器和鑑別器實現

本書適合對象


如果您是數據科學家、機器學習開發人員、深度學習從業者或人工智能愛好者,並且正在尋找一本項目指南來測試您在構建真實世界GAN模型方面的知識和專業技能,那麼本書適合您。

目錄



  1. 生成對抗網絡簡介

  2. 3D-GAN-使用GAN生成形狀

  3. 使用條件GAN進行人臉老化

  4. 使用DCGAN生成動漫角色

  5. 使用SRGAN生成逼真圖像

  6. StackGAN-文本到逼真圖像合成

  7. CycleGAN-將繪畫轉換為照片

  8. 條件GAN-使用條件對抗網絡進行圖像到圖像的轉換

  9. 預測GAN的未來