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https://github.com/nicola-decao/s-vae-pytorch
Pytorch implementation of Hyperspherical Variational Auto-Encoders
https://github.com/nicola-decao/s-vae-pytorch
deep-learning hyperspherical-vae machine-learning manifold-learning pytorch vae variational-autoencoder von-mises-fisher
Last synced: 4 months ago
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Pytorch implementation of Hyperspherical Variational Auto-Encoders
- Host: GitHub
- URL: https://github.com/nicola-decao/s-vae-pytorch
- Owner: nicola-decao
- License: mit
- Created: 2018-06-29T07:52:11.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2020-03-21T16:06:50.000Z (over 4 years ago)
- Last Synced: 2024-01-16T10:46:35.963Z (5 months ago)
- Topics: deep-learning, hyperspherical-vae, machine-learning, manifold-learning, pytorch, vae, variational-autoencoder, von-mises-fisher
- Language: Python
- Homepage: http://arxiv.org/abs/1804.00891
- Size: 27.3 KB
- Stars: 336
- Watchers: 9
- Forks: 57
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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- awesome-stars - nicola-decao/s-vae-pytorch - Pytorch implementation of Hyperspherical Variational Auto-Encoders (Python)
README
# Hyperspherical Variational Auto-Encoders
### Pytorch implementation of Hyperspherical Variational Auto-Encoders
## Overview
This library contains a Pytorch implementation of the hyperspherical variational auto-encoder, or S-VAE, as presented in [[1]](#citation)(http://arxiv.org/abs/1804.00891). Check also our blogpost (https://nicola-decao.github.io/s-vae).
* Don't use Pytorch? Take a look [here](https://github.com/nicola-decao/s-vae-tf) for a **tensorflow** implementation!
## Dependencies
* **python>=3.6**
* **pytorch>=0.4.1**: https://pytorch.org
* **scipy**: https://scipy.org
* **numpy**: https://www.numpy.org
## Installation
To install, run
```bash
$ python setup.py install
```
## Structure
* [distributions](https://github.com/nicola-decao/s-vae-pytorch/tree/master/hyperspherical_vae/distributions): Pytorch implementation of the von Mises-Fisher and hyperspherical Uniform distributions. Both inherit from `torch.distributions.Distribution`.
* [ops](https://github.com/nicola-decao/s-vae-pytorch/tree/master/hyperspherical_vae/ops): Low-level operations used for computing the exponentially scaled modified Bessel function of the first kind and its derivative.
* [examples](https://github.com/nicola-decao/s-vae-pytorch/tree/master/examples): Example code for using the library within a PyTorch project.
## Usage
Please have a look into the [examples folder](https://github.com/nicola-decao/s-vae-pytorch/tree/master/examples). We adapted our implementation to follow the structure of the [Pytorch probability distributions](https://pytorch.org/docs/stable/distributions.html).
Please cite [[1](#citation)] in your work when using this library in your experiments.
## Sampling von Mises-Fisher
To sample the von Mises-Fisher distribution we follow the rejection sampling procedure as outlined by [Ulrich, 1984](http://www.jstor.org/stable/2347441?seq=1#page_scan_tab_contents). This simulation pipeline is visualized below:
_Note that as  is a scalar, this approach does not suffer from the curse of dimensionality. For the final transformation, , a [Householder reflection](https://en.wikipedia.org/wiki/Householder_transformation) is utilized._
## Feedback
For questions and comments, feel free to contact [Nicola De Cao](mailto:[email protected]) or [Tim Davidson](mailto:[email protected]).
## License
MIT
## Citation
```
[1] Davidson, T. R., Falorsi, L., De Cao, N., Kipf, T.,
and Tomczak, J. M. (2018). Hyperspherical Variational
Auto-Encoders. 34th Conference on Uncertainty in Artificial Intelligence (UAI-18).
```
BibTeX format:
```
@article{s-vae18,
title={Hyperspherical Variational Auto-Encoders},
author={Davidson, Tim R. and
Falorsi, Luca and
De Cao, Nicola and
Kipf, Thomas and
Tomczak, Jakub M.},
journal={34th Conference on Uncertainty in Artificial Intelligence (UAI-18)},
year={2018}
}
```