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latent-space

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A comprehensive deep dive into how Variational Autoencoders (VAEs) learn to generate realistic synthetic tabular data. This project explores latent space learning, probabilistic modeling, and neural creativity, combining data privacy, interpretability, and generative AI techniques in a structured format.

  • Updated Nov 10, 2025

mplementation and study of deep generative models including VAE, GAN, and Diffusion. Explores latent representation learning, sampling, and reconstruction using PyTorch. Includes experiments on image generation, comparison of architectures, and evaluation via FID/IS metrics.

  • Updated Oct 16, 2025
  • Jupyter Notebook

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