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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

Concepts

Artificial Intelligence

Authors (2):

Joseph Babcock

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

Unpaired style transfer using CycleGAN

Paired style transfer is a powerful setup with a number of use cases, some of which we discussed in the previous section. It provides the ability to perform cross-domain transfer given a pair of source and target domain datasets. The pix2pix setup also showcased the power of GANs to understand and learn the required loss functions without the need for manually specifying them.

While being a huge improvement over hand-crafted loss functions and previous works, paired style transfer is limited by the availability of paired datasets. Paired style transfer requires the input and output images to be structurally the same even though the domains are different (aerial to map, labels to scene, and so on). In this section, we will focus on an improved style transfer architecture called CycleGAN.

CycleGAN improves upon paired style transfer architecture by relaxing the constraints on input and output images. CycleGAN explores the unpaired style...

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Authors (2)

Joseph Babcock

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Raghav Bali

See other products by Raghav Bali

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