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https://github.com/xnought/vae-explainer

Interactive Variational Autoencoder (VAE)
https://github.com/xnought/vae-explainer

keras ml tfjs

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Interactive Variational Autoencoder (VAE)

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README

        

Learn how Variational Autoencoders (VAE) work by visualizing one running in your browser.

- Live Site: https://xnought.github.io/vae-explainer/
- Paper: https://arxiv.org/abs/2409.09011

https://github.com/user-attachments/assets/ef788f0a-9c66-48fb-99d9-f4feb62ca9cd

## Development

```bash
pnpm install
pnpm dev
```
which runs the hot-reloading dev server at http://localhost:5173

## Cite

```bibtex
@misc{bertucci2024vaeexplainer,
title={VAE Explainer: Supplement Learning Variational Autoencoders with Interactive Visualization},
author={Donald Bertucci and Alex Endert},
year={2024},
eprint={2409.09011},
archivePrefix={arXiv},
primaryClass={cs.HC},
url={https://arxiv.org/abs/2409.09011},
}
```

## Code References

- https://keras.io/examples/generative/vae/
- https://www.ibm.com/think/topics/variational-autoencoder#:~:text=Variational%20autoencoders%20(VAEs)%20are%20generative,other%20autoencoders%2C%20such%20as%20denoising.
- https://www.youtube.com/watch?v=9zKuYvjFFS8
- https://arxiv.org/abs/1312.6114 (original VAE paper)
- https://www.youtube.com/watch?v=afNuE5z2CQ8
- https://keras.io/examples/keras_recipes/trainer_pattern/