Diversity issues in the synthetic data realm
As we have seen, diversity helps us to build robust, accurate, and general-purpose ML models. Additionally, we learned many approaches to improve synthetic data diversity in practice. In this section, we will examine three main issues we usually encounter when we try to generate diverse synthetic data:
- Balancing diversity and realism
- Privacy and confidentiality concerns
- Validation and evaluation challenges
Balancing diversity and realism
There is usually a trade-off between diversity and realism. Generating diverse synthetic examples without considering the realism of these generated samples may introduce or increase the domain gap between synthetic and real domains. For more details, please refer to Chapters 13 and 14. For example, let’s suppose that we want to generate images with sports cars for a particular computer vision task or application. While it is crucial to generate diverse sports cars that cover...
