langevin-dynamics-and-score-matching

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langevin-dynamics-and-score-matching

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A demo shows how to combine Langevin dynamics with score matching for generative models.

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# Langevin Dynamics and Score Matching

## How to?

### Setup

```
pip install -r requirements.txt
```

### Train

```
python3 main.py config/ring.yml train
```

This command produces the model (learnt scaled score function): `ring.pth`

### Test

```
python3 main.py config/ring.yml test
```

This command produces a list of frames at each stage of sampling under `ring/` and an animation `ring.gif` made from them.

## Demo (with carefully tuned hyperparameters)

- Left: sample from the real pixel distribution
- Right: sample using Langevin dynamics based on the score function learnt via denoising score matching.

### Ring

![ring.gif](https://raw.githubusercontent.com/enhuiz/langevin-dynamics-and-score-matching/main/ring.gif)

### Waddles

![waddles.gif](https://raw.githubusercontent.com/enhuiz/langevin-dynamics-and-score-matching/main/waddles.gif)

### Stewie

![stewie.gif](https://raw.githubusercontent.com/enhuiz/langevin-dynamics-and-score-matching/main/stewie.gif)

## Reference

- A good [lecture](https://youtu.be/3-KzIjoFJy4) on Langevin dynamics for sampling.
- A [recent paper](https://arxiv.org/pdf/1907.05600.pdf) combining Langevin dynamics with denosing score matching with a more complicated multi-level pertubation scheme.
- A [paper](http://www.iro.umontreal.ca/~vincentp/Publications/smdae_techreport.pdf) introducing the denoising score matching.

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