Synthetic Data Generation is the process of algorithmically producing artificial datasets that statistically mirror real-world distributions without exposing sensitive personal information. Techniques include generative adversarial networks, diffusion models, physics simulation, and rule-based sampling, enabling model training where real data is scarce, private, or costly to label.
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0422 Synthetic Data Generation — content pending enrichment.