Synthcity is an open-source synthetic data generation library that outperforms rivals (YData, Gretel, SDV, etc.) in terms of compatible use cases and data modalities, offering solutions for privacy, data scarcity, and fairness across various data types.
How is it unique?
Synthcity gathers state-of-the-art generative models into one user-friendly platform, supporting a wide range of data modalities, such as tabular, time series, censored datasets, and images. It combines cutting-edge techniques from Generative Adversarial Networks (GANs), Variational Auto-Encoders (VAEs), Normalizing Flows, Graphical Neural Networks (GNNs), and Diffusion Models. It is our biggest open-source project to date.
How is it useful?
Synthcity can, among other use cases:
1. Address data privacy concerns by generating synthetic datasets that preserve the original data’s patterns while protecting sensitive information.
2. Combat data scarcity by generating realistic, high-quality synthetic data to improve model training, validation, and performance.
3. Ensure fairness in ML models by generating balanced datasets that mitigate biases, leading to more equitable treatment and drug development outcomes.
4. Facilitate rapid experimentation, prototyping, and benchmarking with a comprehensive suite of evaluation metrics, such as inverse KL divergence, Jensen-Shannon distance, survival KM distance, and many more.
Synthcity‘s versatile models and evaluation metrics make it an invaluable tool for the research community and industry alike, facilitating innovation, safeguarding of privacy, and ensuring of fairness in many data-driven initiatives. We believe that by leveraging Synthcity‘s capabilities, the impact of data science and AI in healthcare can be greatly sped up and enhanced.