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Generative AI with Python and TensorFlow 2

You're reading from   Generative AI with Python and TensorFlow 2 Create images, text, and music with VAEs, GANs, LSTMs, Transformer models

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781800200883
Length 488 pages
Edition 1st Edition
Languages
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Authors (2):
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Raghav Bali Raghav Bali
Author Profile Icon Raghav Bali
Raghav Bali
Joseph Babcock Joseph Babcock
Author Profile Icon Joseph Babcock
Joseph Babcock
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Toc

Table of Contents (16) Chapters Close

Preface 1. An Introduction to Generative AI: "Drawing" Data from Models 2. Setting Up a TensorFlow Lab FREE CHAPTER 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

Summary

Deepfakes are a complicated subject both ethically and technically. In this chapter, we discussed the deepfake technology in general to start with. We presented an overview of what deepfakes are all about and briefly touched upon a number of productive as well as malicious use cases. We presented a detailed discussion on different modes of operation of different deepfake setups and how each of these impacts the overall believability of generated content. While deepfakes is an all-encompassing term associated with videos, images, audio, text, and so on, we focused on visual use cases only in this chapter.

Given our scope, we discussed various feature sets leveraged by different works in this space. In particular, we discussed the Facial Action Coding System (FACS), 3D Morphable Models (3DMM), and facial landmarks. We also discussed how we can perform facial landmark detection using libraries such as dlib and MTCNN. We then presented a high-level flow of tasks to be performed...

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