altosaar/variational-autoencoder

Health v2 · maintenance only

Adoption not part of the score

1192 stars · 258 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A reference implementation of a variational autoencoder (VAE) in PyTorch, TensorFlow, and JAX, including an inverse autoregressive flow variational posterior. It trains on binarized MNIST and accompanies a well-known tutorial on variational autoencoders.

Use cases

• learn how variational autoencoders work with runnable code
• implement a VAE in pytorch
• train a VAE with inverse autoregressive flow
• reproduce published MNIST marginal likelihood results
• compare VAE implementations across tensorflow, pytorch, and jax
• study variational inference and amortized inference

When to choose

• you want a clean, minimal reference VAE implementation for learning or research
• you need an example of inverse autoregressive flow posteriors
• you want to reproduce benchmark log-likelihood numbers on binarized MNIST

When to avoid

• you need a production-ready generative model for real-world images
• you want a high-level API or pretrained models
• you need actively developed features or support

Facets

library · maturity maintenance

machine-learning deep-learning machine-learning deep-learning artificial-intelligence python variational-autoencoder variational-inference pytorch tensorflow jax inverse-autoregressive-flow generative-models mnist reference-implementation gpu

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