It's not perfect. You do need real world data. Synthetic data is great for creating edge cases. Say you want your model to classify more general use cases, but your real world data is limited. You can generate data that fits edge cases with some variance and the model will accept more variety, reducing over fitting.
It's not perfect. You do need real world data. Synthetic data is great for creating edge cases. Say you want your model to classify more general use cases, but your real world data is limited. You can generate data that fits edge cases with some variance and the model will accept more variety, reducing over fitting.