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Synthetic Data for Machine Learning

You're reading from   Synthetic Data for Machine Learning Revolutionize your approach to machine learning with this comprehensive conceptual guide

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781803245409
Length 208 pages
Edition 1st Edition
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Author (1):
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Abdulrahman Kerim Abdulrahman Kerim
Author Profile Icon Abdulrahman Kerim
Abdulrahman Kerim
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Table of Contents (25) Chapters Close

Preface 1. Part 1:Real Data Issues, Limitations, and Challenges
2. Chapter 1: Machine Learning and the Need for Data FREE CHAPTER 3. Chapter 2: Annotating Real Data 4. Chapter 3: Privacy Issues in Real Data 5. Part 2:An Overview of Synthetic Data for Machine Learning
6. Chapter 4: An Introduction to Synthetic Data 7. Chapter 5: Synthetic Data as a Solution 8. Part 3:Synthetic Data Generation Approaches
9. Chapter 6: Leveraging Simulators and Rendering Engines to Generate Synthetic Data 10. Chapter 7: Exploring Generative Adversarial Networks 11. Chapter 8: Video Games as a Source of Synthetic Data 12. Chapter 9: Exploring Diffusion Models for Synthetic Data 13. Part 4:Case Studies and Best Practices
14. Chapter 10: Case Study 1 – Computer Vision 15. Chapter 11: Case Study 2 – Natural Language Processing 16. Chapter 12: Case Study 3 – Predictive Analytics 17. Chapter 13: Best Practices for Applying Synthetic Data 18. Part 5:Current Challenges and Future Perspectives
19. Chapter 14: Synthetic-to-Real Domain Adaptation 20. Chapter 15: Diversity Issues in Synthetic Data 21. Chapter 16: Photorealism in Computer Vision 22. Chapter 17: Conclusion 23. Index 24. Other Books You May Enjoy

Diffusion models – ethical issues

In this section, you will learn about the main ethical issues associated with using DMs for synthetic data generation.

Diffusion-based generative models are emerging and powerful technologies. Thus, their pros and cons need to be considered carefully. Their advantages are huge for businesses, industry, and research. However, they possess dangerous capabilities that can be leveraged to cause harm to individuals, businesses, societies, and so on.

Let’s list the main ethical issues usually associated with generative models and, especially, DMs:

  • Copyright
  • Bias
  • Inappropriate content
  • Responsibility issues
  • Privacy issues
  • Fraud and identity theft

Now, let’s delve into some of the main ethical issues behind using DMs in practice.

Copyright

DMs are usually trained on large-scale real datasets. For example, DALL E 2 was trained on more than 650 million text-image pairs. Thus, obtaining permission...

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