How Synthetic Data Is Generated
A technical overview of synthetic data generation methods: GANs, VAEs, CTGAN, diffusion models, and rule-based approaches — how they work and when to use them.
Synthetic data generation is the process of training a generative model on real data and then sampling from that model to produce new, artificial records that statistically mirror the original.
The choice of generation method depends on data type (tabular, image, text, time series), fidelity requirements, privacy constraints, and compute budget.
GANs — Generative Adversarial Networks
GANs consist of two competing neural networks: a generator that creates synthetic samples, and a discriminator that attempts to distinguish real from synthetic. Through adversarial training, the generator learns to produce samples that are indistinguishable from real data.
CTGAN — Conditional Tabular GAN
CTGAN is a GAN architecture specifically designed for tabular data. It handles mixed data types (numerical and categorical), imbalanced columns, and multi-modal distributions through conditional sampling and mode-specific normalization.
VAEs — Variational Autoencoders
VAEs encode data into a latent probability distribution, then sample from that distribution to generate new instances. They offer smoother latent spaces and more stable training than GANs, but may produce less crisp outputs.
Diffusion Models
Diffusion models generate data by progressively denoising random noise into structured samples. Originally developed for images, they are increasingly applied to tabular and sequential data.
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