Hands-On Generative Adversarial Networks with PyTorch 1.x

Hany, John (Author), Walters, Greg (Autho

  • 出版商: Packt Publishing
  • 出版日期: 2019-12-12
  • 售價: $1,480
  • 貴賓價: 9.5$1,406
  • 語言: 英文
  • 頁數: 312
  • ISBN: 1789530512
  • ISBN-13: 9781789530513
  • 相關分類: DeepLearning
  • 立即出貨 (庫存=1)



  • Implement PyTorch's latest features to ensure efficient model designing
  • Get to grips with the working mechanisms of GAN models
  • Perform style transfer between unpaired image collections with CycleGAN
  • Build and train 3D-GANs to generate a point cloud of 3D objects
  • Create a range of GAN models to perform various image synthesis operations
  • Use SEGAN to suppress noise and improve the quality of speech audio

With continuously evolving research and development, Generative Adversarial Networks (GANs) are the next big thing in the field of deep learning. This book highlights the key improvements in GANs over generative models and guides in making the best out of GANs with the help of hands-on examples.

This book starts by taking you through the core concepts necessary to understand how each component of a GAN model works. You'll build your first GAN model to understand how generator and discriminator networks function. As you advance, you'll delve into a range of examples and datasets to build a variety of GAN networks using PyTorch functionalities and services, and become well-versed with architectures, training strategies, and evaluation methods for image generation, translation, and restoration. You'll even learn how to apply GAN models to solve problems in areas such as computer vision, multimedia, 3D models, and natural language processing (NLP). The book covers how to overcome the challenges faced while building generative models from scratch. Finally, you'll also discover how to train your GAN models to generate adversarial examples to attack other CNN and GAN models.

By the end of this book, you will have learned how to build, train, and optimize next-generation GAN models and use them to solve a variety of real-world problems.

  • Implement GAN architectures to generate images, text, audio, 3D models, and more
  • Understand how GANs work and become an active contributor in the open source community
  • Learn how to generate photo-realistic images based on text descriptions




  • 實現PyTorch的最新功能,以確保有效的模型設計

  • 了解GAN模型的工作機制

  • 使用CycleGAN在不成對的圖像集合之間進行風格轉換

  • 構建和訓練3D-GANs以生成3D物體的點雲

  • 創建各種GAN模型,執行各種圖像合成操作

  • 使用SEGAN壓制噪音,改善語音音頻的質量






  • 實現GAN架構以生成圖像、文本、音頻、3D模型等

  • 了解GAN的工作原理,成為開源社區的積極貢獻者

  • 學習如何根據文本描述生成逼真的圖像