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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

Generating synthetic data

In this section, we will learn how to generate synthetic data using modern game engines such as Unity and Unreal.

To generate synthetic data with its corresponding ground truth, it is recommended that we follow these steps:

  1. Identify the task and ground truth to generate.
  2. Create the 3D virtual world in the game engine.
  3. Set the virtual camera.
  4. Add noise and anomalies.
  5. Set the labeling pipeline.
  6. Generate the training data with the ground truth.

Throughout this section, we will thoroughly discuss each facet of this process.

Identify the task and ground truth to generate

The first step in the synthetic data generation process is defining the task, the type of the data, and the ground truth to generate. For example, the data could be images, videos, or audio. At the same time, you need to identify what ground truth to generate for your problem. For example, you can generate semantic segmentation, instance segmentation...

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