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Expand Up @@ -17,6 +17,7 @@ Inspired by [Awesome Synthetic Data](https://github.com/gretelai/awesome-synthet
+ [Copulas](https://github.com/sdv-dev/Copulas): a Python library for modeling multivariate distributions and sampling from them using copula functions.
+ [CTGAN](https://github.com/sdv-dev/CTGAN): SDV’s collection of deep learning-based synthetic data generators for single table data.
+ [DataGene](https://github.com/firmai/datagene): a tool to train, test, and validate datasets, detect and compare dataset similarity between real and synthetic datasets.
+ [distilabel](https://github.com/argilla-io/distilabel): Distilabel is a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.
+ [DoppelGANger](https://github.com/fjxmlzn/DoppelGANger): a synthetic data generation framework based on generative adversarial networks (GANs).
+ [DP_WGAN-UCLANESL](https://github.com/nesl/nist_differential_privacy_synthetic_data_challenge): this solution trains a Wasserstein generative adversarial network (w-GAN) that is trained on the real private dataset.
+ [DPSyn](https://github.com/usnistgov/PrivacyEngCollabSpace/tree/master/tools/de-identification/Differential-Privacy-Synthetic-Data-Challenge-Algorithms/DPSyn): an algorithm for synthesizing microdata while satisfying differential privacy.
Expand All @@ -30,6 +31,7 @@ Inspired by [Awesome Synthetic Data](https://github.com/gretelai/awesome-synthet
+ [Synner](https://github.com/huda-lab/synner): an open source tool to generate real-looking synthetic data by visually specifying the properties of the dataset.
+ [Synth](https://www.getsynth.com/): an open source data-as-code tool that provides a simple CLI workflow for generating consistent data in a scalable way.
+ [Synthea](https://synthetichealth.github.io/synthea/): an open source synthetic patient generator that models the medical history of synthetic patients.
+ [Synthetic Data Generator](https://huggingface.co/spaces/argilla/synthetic-data-generator): Build datasets using natural language for free using Hugging Face inference endpoints, Gradio and distilabel.
+ [Synthetic data vault (SDV)](https://sdv.dev/): one of the first open source synthetic data solutions, SDV provides tools for generating synthetic data for tabular, relational, and time series data.
+ [TGAN](https://github.com/sdv-dev/TGAN): generative adversarial training for generating synthetic tabular data.
+ [Tofu](https://github.com/spiros/tofu): a Python library for generating synthetic UK Biobank data.
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