mdhabibi/DeepLearning-VAE
Exploring the depths of generative learning with a $\beta$-Variational Autoencoder ($\beta$-VAE) applied to the MNIST dataset for robust digit reconstruction and latent space analysis.
This project helps machine learning practitioners understand and experiment with Variational Autoencoders (VAEs) for generating new image data. It takes images of handwritten digits as input and outputs reconstructed versions of those digits, along with new, similar-looking digits. Researchers or students in machine learning and computer vision would use this to grasp how generative models learn and create images.
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Use this if you are studying generative AI and want to see how VAEs work for image reconstruction and new image generation, especially for simple image datasets like handwritten digits.
Not ideal if you need a production-ready system for complex image generation or anomaly detection in high-resolution images, as this is an exploratory project focused on a foundational dataset.
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Jupyter Notebook
License
MIT
Last pushed
Jun 01, 2024
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