---
id: synthetic-data-training
title: Synthetic Data in AI Training
schema_type: TechArticle
category: ai
language: en
confidence: medium
last_verified: "2026-05-28"
created_date: "2026-05-24"
generation_method: ai_structured
ai_models:
  - claude-opus
derived_from_human_seed: true
conflict_of_interest: none_declared
is_live_document: false
data_period: static
atomic_facts:
  - id: fact-synthetic-data-1
    statement: SMOTE creates synthetic minority-class examples for imbalanced classification datasets.
    source_title: "SMOTE: Synthetic Minority Over-sampling Technique"
    source_url: https://doi.org/10.1613/jair.953
    confidence: medium
  - id: fact-synthetic-data-2
    statement: >-
      Generative adversarial networks train a generator and discriminator in a minimax game to
      produce synthetic samples.
    source_title: Generative Adversarial Networks
    source_url: https://arxiv.org/abs/1406.2661
    confidence: medium
  - id: fact-synthetic-data-3
    statement: >-
      Domain randomization trains models in varied simulations to help transfer to real-world
      settings.
    source_title: Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
    source_url: https://arxiv.org/abs/1703.06907
    confidence: medium
completeness: 0.9
known_gaps:
  - This compact repair keeps only source-mapped public claims from the sampled audit entry.
disputed_statements: []
primary_sources:
  - title: "SMOTE: Synthetic Minority Over-sampling Technique"
    type: academic_paper
    year: 2002
    url: https://doi.org/10.1613/jair.953
    doi: 10.1613/jair.953
    institution: Journal of Artificial Intelligence Research
  - title: Generative Adversarial Networks
    type: academic_paper
    year: 2014
    url: https://arxiv.org/abs/1406.2661
    institution: NeurIPS / arXiv
  - title: Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
    type: academic_paper
    year: 2017
    url: https://arxiv.org/abs/1703.06907
    institution: arXiv
secondary_sources: []
updated: "2026-05-28"
---

## TL;DR

Synthetic data training uses generated or simulated data to augment scarce, imbalanced, or safety-constrained datasets. This repair maps claims to SMOTE, GANs, and domain randomization.

## Core Explanation

The sampled entry had partial source coverage. This version keeps three source-backed synthetic-data techniques.

## Further Reading

- [SMOTE: Synthetic Minority Over-sampling Technique](https://doi.org/10.1613/jair.953)
- [Generative Adversarial Networks](https://arxiv.org/abs/1406.2661)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://arxiv.org/abs/1703.06907)