Intermediate · Data

Synthetic Data Generation

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. The process of computationally creating artificial data that mimics the statistical properties and patterns of real-world data.

Technical Definition

The process of computationally creating artificial data that mimics the statistical properties and patterns of real-world data.

How it works

Synthetic data generation offers solutions for data privacy, augmentation of limited datasets, and balancing biases. Generative models like GANs and VAEs are often used to create realistic synthetic data. While offering significant advantages, careful validation is needed to ensure the generated data accurately represents real-world variability and doesn't introduce new artifacts or biases.

Related Concepts

• Data Augmentation — Techniques that artificially expand training datasets by applying transformations to existing samples.
• Synthetic Data — Artificially generated data mimicking real-world properties, used for training augmentation or privacy protection.
• GAN — Abbreviation for Generative Adversarial Network, a class of machine learning frameworks.