Intermediate · Fundamentals

Synthetic Data

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

TL;DR. Artificially generated data mimicking real-world properties, used for training augmentation or privacy protection.

Technical Definition

Artificially generated data mimicking real-world properties, used for training augmentation or privacy protection.

How it works

Synthetic data is created algorithmically using GANs, diffusion models, or LLMs. Applications include augmenting scarce datasets, privacy-preserving alternatives, simulation for robotics, and red-teaming AI systems.

Related Concepts

• Generative Adversarial Network (GAN) — Two neural networks — a generator and discriminator — compete against each other to produce increasingly realistic synthetic data.
• Diffusion Model — A generative model that learns to create data by reversing a gradual noising process, producing high-quality images and audio.
• Data Augmentation — Techniques that artificially expand training datasets by applying transformations to existing samples.