Implementing a Variational Autoencoder (VAE) Series in Pytorch.
Inspired by this repository
| check | model | paper | conference |
|---|---|---|---|
| O | VAE | Auto-Encoding Variational Bayes | ICLR 2014 |
| O | CVAE | Learning Structured Output Representation using Deep Conditional Generative Models | NeurIPS 2015 |
| O | AAE | Adversarial Autoencoder | ICLR 2016 |
| O | Beta-VAE | β-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework | ICLR 2017 |
| VQ-VAE | Neural Discrete Representation Learning | NeurIPS 2017 |
TBD
If you have any question about the code, feel free to email me at subinium@gmail.com.