https://github.com/abdulfatir/planar-flow-pytorch

Pytorch implementation of Planar Flow
https://github.com/abdulfatir/planar-flow-pytorch

normalizing-flow variational-autoencoder

Last synced: about 1 year ago
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Pytorch implementation of Planar Flow

• Host: GitHub
• URL: https://github.com/abdulfatir/planar-flow-pytorch
• Owner: abdulfatir
• License: mit
• Created: 2019-12-02T09:48:24.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-12-02T20:42:32.000Z (over 6 years ago)
• Last Synced: 2025-03-31T00:41:17.417Z (about 1 year ago)
• Topics: normalizing-flow, variational-autoencoder
• Language: Jupyter Notebook
• Homepage:
• Size: 183 KB
• Stars: 17
• Watchers: 2
• Forks: 14
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
## Planar Flow

This repo contains a Pytorch implementation of Planar Flow presented in (Rezende and Mohamed, 2015) with experiments on a 2D density and MNIST dataset.

### 2D Density Results

![](assets/2ddensity.png)

### MNIST Results

Input | Model | Latent Space Size | Test Lower Bound |

:--------:|:-----------------|:---:|:------:

[0,1]     |    VAE           |  20 | -99.37 

[0,1]     |    VAE+PF (K=20) |  20 | -98.23

{0,1}     |    VAE           |  20 | -84.60

{0,1}     |    VAE+PF (K=20) |  20 | -81.83

[0,1] denotes float values between 0 and 1 and {0,1} denotes binary values.

#### Usage

Vanilla VAE: `python vae.py`    

VAE with Planar Flow: `python vae-pf.py`

Add `--binary` option to binarize the input dataset.

### References

(Rezende and Mohamed, 2015) Rezende, Danilo, and Shakir Mohamed. "Variational Inference with Normalizing Flows." International Conference on Machine Learning. 2015.