awesome-data-synthesis

R Package to generate synthetic data.

Repository of a software system for generating synthetic personal data based on the Bayesian network block structure

Generating High-Fidelity Synthetic Patient Data for Assessing Machine Learning Healthcare Software -

Synthetic data generation with probabilistic Bayesian Networks -

categorical latent Gaussian process is a generative model for multivariate categorical data -

Correlation-Capturing Convolutional Neural Networks for Generating Synthetic Healthcare Records -

CTGAN is a GAN-based data synthesizer that can generate synthetic tabular data with high fidelity. -

Synthetic Data Gym (SDGym) is a framework to benchmark the performance of synthetic data generators for tabular data. SDGym is a project of the Data to AI Laboratory at MIT.

A methodology designed to validate the statistical similarity of synthetic data generated by GAN models. The metrics contain Auto-encoder, PCA, t-SNE, KL-divergence, Clustering, and Cosine Similarity.