Machine learning models, particularly those deployed on edge devices, often face challenges when transitioning from development to real-world environments. Despite rigorous training, models may struggle with unexpected conditions, insufficiently represented scenarios, or limitations in data diversity. Expanding a dataset through additional data collection can be costly and time-consuming. This is where synthetic data comes in.
This is a companion discussion topic for the original entry at https://www.edgeimpulse.com/blog/synthetic-data-and-you-integrating-synthetic-data-into-your-edge-ai-dataset