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CTGANgithub.comCTGAN is a collection of deep learning-based synthetic data generators for single-table data that learn from real data to produce high-fidelity synthetic data. It implements the CTGAN and TVAE models described in the Modeling Tabular Data Using Conditional GANs paper (NeurIPS 2019) and is part of the Synthetic Data Vault (SDV) project by DataCebo. It can be used as a standalone library or via the SDV library, with Python APIs for data preprocessing and generation, including examples like the Adult Census Dataset.
Sources
arxiv.org shapes more of what AI says about CTGAN than any other source, at 16% of its citations.
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Synthetic Data Generation Platforms →Where AI ranks CTGAN
Excerpts where CTGAN appeared in the AI's answer
CTGAN / CopulaGAN are popular, standalone open-source algorithms built for tabular data containing complex distributions and non-Gaussian characteristics
CTGAN (Conditional GAN): A popular choice for datasets with heavily imbalanced discrete columns, often considered an improvement over standard GANs for tabular data.
