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

Hands-On Image Generation with TensorFlow: A practical guide to generating images and videos using deep learning
by: Soon Yau Cheong
Publisher Finelybook 出版社: Packt Publishing (December 24,2020)
Language 语言: English
Paperback: 306 pages
ISBN-10: 1838826785
ISBN-13: 9781838826789
File size: PDF,EPUB,MOBI,Code
Book Description
Implement various state-of-the-art architectures,such as GANs and autoencoders,for image generation using TensorFlow 2.x from scratch
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