How does synthetic data help in overcoming domain gaps in machine learning?

Synthetic data helps bridge domain gaps by providing consistent and diverse data that may not be readily available in real-world scenarios. For example, if your real-world data lacks certain conditions or variations, synthetic data can fill in those gaps, ensuring your machine learning model is trained on a more comprehensive dataset. This leads to better generalization and performance when the model is deployed in real-world applications.