MahanVeisi8/VAE-MNIST-Variable-Latent-Size-Reconstruction-and-Visualization
Dive into the world of Variational Autoencoders (VAEs) with MNIST! 🎨✨ Explore variable latent sizes (2, 4, 16) to see how they affect reconstruction, latent space visualizations, and performance metrics 📊 (MSE, SSIM, PSNR).
This project helps deep learning practitioners understand how Variational Autoencoders (VAEs) work. It takes images, specifically handwritten digits like those from the MNIST dataset, and shows how a VAE can compress them into a 'latent space' and then reconstruct them. The output includes reconstructed images, visualizations of the latent space, and performance metrics. It's designed for researchers, students, and engineers who are exploring generative models and data compression.
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Use this if you want to explore the impact of different latent space sizes on image reconstruction quality and visualize how a VAE organizes data in its hidden dimensions.
Not ideal if you need a production-ready solution for generating complex, high-resolution images or if you're looking for an application-specific generative model beyond basic image data.
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Jan 22, 2025
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