Synthetic Data

Definition

Synthetic data is artificially generated information that mimics real-world data. It can be created using simulations, GANs, or other generative methods.

Purpose

The purpose is to augment or replace real data when it is scarce, sensitive, or expensive to collect.

Importance

  • Protects privacy by reducing reliance on personal data.
  • Enables training for rare or edge cases.
  • May lack the full complexity of real-world data.
  • Increasingly used in safety-critical AI.

How It Works

  1. Define the data characteristics to replicate.
  2. Use simulation or generative models to create data.
  3. Validate synthetic data against real distributions.
  4. Use synthetic data in training pipelines.
  5. Monitor for gaps in realism.

Examples (Real World)

  • Waymo: uses synthetic driving scenes for autonomous training.
  • NVIDIA Omniverse: generates synthetic 3D data for robotics.
  • Healthcare: synthetic patient data for research.

References / Further Reading

  • NIST Special Publication on Synthetic Data.
  • Goncalves et al. “Generation and Evaluation of Synthetic Data.” ACM Computing Surveys.
  • Synthetic Data Vault (MIT).