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Generative Adversarial Networks Projects

You're reading from  Generative Adversarial Networks Projects

Product type Book
Published in Jan 2019
Publisher Packt
ISBN-13 9781789136678
Pages 316 pages
Edition 1st Edition
Languages
Author (1):
Kailash Ahirwar Kailash Ahirwar
Profile icon Kailash Ahirwar

Table of Contents (11) Chapters

Preface 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 10. Other Books You May Enjoy

Introducing Pix2pix

Pix2pix is a variant of the conditional GAN. We have already covered conditional GANs in Chapter 3, Face-Aging Using Conditional GAN (cGAN). Before moving forward, make sure you take a look at what cGANs are. Once you are comfortable with cGANs, you can continue with this chapter. Pix2pix is a type of GAN that is capable of performing image-to-image translation using the unsupervised method of machine learning (ML). Once trained, pix2pix can translate an image from domain A to domain B. Vanilla CNNs can also be used for image-to-image translation, but they don't generate realistic and sharp images. On the other hand, pix2pix has shown immense potential to be able to generate realistic and sharp images. We will be training pix2pix to translate labels of facades to images of facade. Let's start by understanding the architecture of pix2pix.

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