Challenges and limitations

To ensure that you are aware of common issues that usually hinder the effective utilization of synthetic data, we comprehensively explored synthetic-to-real domain adaptation approaches. We thoroughly studied the domain gap problem in ML and learned about the main approaches for synthetic-to-real domain adaptation (Chapter 14). Then, we learned why diverse data is essential in ML and discovered the main strategies to generate diverse synthetic datasets. Following this, we highlighted the main issues and challenges of generating diverse synthetic data (Chapter 15). After that, we learned why generating photorealistic data is pivotal in computer vision. We also learned about the main approaches to enhancing photorealism and discussed the essential photorealism evaluation metrics. Then, we covered the challenges and limitations of generating photorealistic synthetic data in practice (Chapter 16).