What is synthetic data?
Synthetic data is created rather than collected. It can come from 3D simulation and rendering engines, from generative models such as diffusion models, or from procedural rules, and it is designed to resemble real data closely enough to be useful for training or evaluation. Because you control the generator, you can produce perfectly labeled examples and deliberately cover rare or dangerous scenarios.
Its main limitation is the domain gap: a model trained only on synthetic data may struggle on real inputs if the two differ in subtle ways.
Key takeaways
- Synthetic data is generated by simulation or models, not collected.
- It provides free, exact labels and controllable edge cases.
- A domain gap can hurt models that train only on synthetic data.
How it works
A generator, whether a simulator or a generative model, produces samples along with their ground-truth labels. Teams often blend synthetic and real data, or apply domain randomization to vary textures, lighting, and layouts so the model does not overfit to synthetic quirks. Closing the domain gap is closely tied to the sim-to-real problem in robotics.
Why it matters
Real data for rare, unsafe, or privacy-sensitive situations is hard to gather, and synthetic data fills those gaps cheaply and safely. It is central to physical AI, where collecting real-world robot data at scale is slow and costly, and to bootstrapping datasets for brand-new tasks.
Frequently asked questions
Can a model train on synthetic data alone?
Sometimes, but the domain gap often means blending in real data or randomizing the generator is needed for strong real-world performance.
How is synthetic data different from data augmentation?
Augmentation transforms existing real samples, while synthetic data is generated from scratch by a simulator or model.
Related terms