Autocurator-Synthetic-Data-Benchmark

Autocurator is a comprehensive benchmarking toolkit for evaluating synthetic tabular data. It measures fidelity, coverage, privacy, and utility through quantitative metrics, visual reports, and PCA/correlation diagnostics. Ideal for validating VAE, GAN, Copula, or Diffusion-generated datasets.

utility

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20 Stars

1 Forks

20 Watchers

Python Language

mit License

100 SrcLog Score

Cost to Build

$2.4K

Market Value

$3.5K

How is this calculated?

Growth over time

2 data points · 2026-04-14 → 2026-04-21

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What is the AmirhosseinHonardoust/Autocurator-Synthetic-Data-Benchmark GitHub project? Description: "Autocurator is a comprehensive benchmarking toolkit for evaluating synthetic tabular data. It measures fidelity, coverage, privacy, and utility through quantitative metrics, visual reports, and PCA/correlation diagnostics. Ideal for validating VAE, GAN, Copula, or Diffusion-generated datasets.". Written in Python. Explain what it does, its main use cases, key features, and who would benefit from using it.

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git clone https://github.com/AmirhosseinHonardoust/Autocurator-Synthetic-Data-Benchmark.git

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