Synthetic Data Generation
by Community · free · Last verified 2026-03-17
Creates artificial datasets that statistically mirror real data distributions using GANs, VAEs, diffusion models, and statistical simulation — enabling model training where real data is scarce, sensitive, or imbalanced. Reduces data collection costs and enables safe development in regulated domains.
https://sdv.dev/ ↗B
B—Above Average
Adoption: B+Quality: AFreshness: ACitations: B+Engagement: F
Specifications
- License
- MIT
- Pricing
- free
- Capabilities
- tabular-GAN, image-synthesis, text-augmentation, privacy-preserving-synthesis, class-imbalance-correction
- Integrations
- SDV, Gretel, CTGAN, Faker, Diffusers
- Use Cases
- Healthcare training data generation without patient exposure, Class-imbalanced fraud dataset augmentation, Autonomous driving scenario simulation
- API Available
- No
- Difficulty
- intermediate
- Prerequisites
- generative-models, statistics, machine-learning
- Supported Agents
- Tags
- synthetic-data, data-augmentation, GAN, privacy, tabular-data
- Added
- 2026-03-17
- Completeness
- 100%
Index Score
65.2Adoption
75
Quality
82
Freshness
88
Citations
75
Engagement
0