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Generative AI with Python and TensorFlow 2

You're reading from  Generative AI with Python and TensorFlow 2

Product type Book
Published in Apr 2021
Publisher Packt
ISBN-13 9781800200883
Pages 488 pages
Edition 1st Edition
Languages
Authors (2):
Joseph Babcock Joseph Babcock
Profile icon Joseph Babcock
Raghav Bali Raghav Bali
Profile icon Raghav Bali
View More author details

Table of Contents (16) Chapters

Preface 1. An Introduction to Generative AI: "Drawing" Data from Models 2. Setting Up a TensorFlow Lab 3. Building Blocks of Deep Neural Networks 4. Teaching Networks to Generate Digits 5. Painting Pictures with Neural Networks Using VAEs 6. Image Generation with GANs 7. Style Transfer with GANs 8. Deepfakes with GANs 9. The Rise of Methods for Text Generation 10. NLP 2.0: Using Transformers to Generate Text 11. Composing Music with Generative Models 12. Play Video Games with Generative AI: GAIL 13. Emerging Applications in Generative AI 14. Other Books You May Enjoy
15. Index

Paired style transfer using pix2pix GAN

In Chapter 6, Image Generation with GANs, we discussed a number of innovations related to GAN architectures that led to improved results and better control of the output class. One of those innovations was conditional GANs. This simple yet powerful addition to the GAN setup enabled us to navigate the latent vector space and control the generator to generate specific outputs. We experimented with a simple MNIST conditional GAN where we were able to generate the output of our choice.

In this section, we will cover a variant of conditional GANs in the context of style transfer. We will go through details of the pix2pix architecture, discuss the important components and also train a paired style transfer network of our own. We will close this section with some amazing and innovative use cases of such a capability.

Style transfer is an intriguing research area, pushing the boundaries of creativity and deep learning together. In their work...

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