zhihanyang2022/aevb-tutorial
Minimal VAE, Conditional VAE (CVAE), Gaussian Mixture VAE (GMVAE) and Variational RNN (VRNN) in PyTorch, trained on MNIST.
This project helps machine learning researchers and students understand and implement advanced generative models. It provides PyTorch code for various Auto-encoding Variational Bayes (AEVB) models like VAE, CVAE, GMVAE, and VRNN. You can input raw data, primarily image data like MNIST, and get out models capable of generating new, similar data or learning structured representations. This is for anyone studying or working with probabilistic deep learning and generative modeling.
No commits in the last 6 months.
Use this if you are a machine learning researcher or student who wants to learn the practical implementation of various Auto-encoding Variational Bayes models.
Not ideal if you are looking for a ready-to-use application for a specific domain like image generation for marketing or financial data analysis.
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Language
Jupyter Notebook
License
MIT
Last pushed
Aug 28, 2022
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