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AmirhosseinHonardoust / Measuring-The-Soul-of-Data

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A narrative and technical exploration of data authenticity through the four pillars of synthetic data realism, Fidelity, Coverage, Privacy, and Utility. This thought-leadership piece combines storytelling, mathematics, and code to explain how these metrics define the ethical and functional “soul” of data in AI systems.

python data-science benchmark coverage machine-learning utility privacy evaluation gan vae copula diffusion data-quality fidelity synthetic-data explainable-ai ai-ethics responsible-ai generative-ai autocurator
  • Updated Nov 14, 2025

AmirhosseinHonardoust / Autocurator-Synthetic-Data-Benchmark

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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.

python data-science benchmark coverage machine-learning utility privacy deep-learning metrics data-validation evaluation pytorch gan vae data-privacy copula diffusion data-quality fidelity synthetic-data
  • Updated Jul 26, 2026
  • Python

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