Synthetic data can be used to train machine learning models effectively, alleviate biases, account for properties not found in real data, and protect privacy. It is used to generate synthetic face datasets with subject uniqueness, style diversity, and label consistency. The goal is to eventually have fully synthetic datasets without real data.
