pytorch-vae and pytorch-vq-vae
These are complementary implementations exploring different VAE architectures—the basic VAE provides a foundation for understanding variational inference, while VQ-VAE extends it with vector quantization to learn discrete latent representations, making them useful for different use cases rather than interchangeable alternatives.
Maintenance
0/25
Adoption
10/25
Maturity
16/25
Community
24/25
Maintenance
0/25
Adoption
10/25
Maturity
16/25
Community
22/25
Stars: 432
Forks: 107
Downloads: —
Commits (30d): 0
Language: Python
License: BSD-3-Clause
Stars: 602
Forks: 102
Downloads: —
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
Stale 6m
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No Dependents
Stale 6m
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No Dependents
About pytorch-vae
ethanluoyc/pytorch-vae
A Variational Autoencoder (VAE) implemented in PyTorch
About pytorch-vq-vae
zalandoresearch/pytorch-vq-vae
PyTorch implementation of VQ-VAE by Aäron van den Oord et al.
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