https://github.com/fer14/tiny_vae

Variational AutoEncoder Tiny Model 🤏
https://github.com/fer14/tiny_vae

tiny vae variational-autoencoder visualization

Last synced: 11 months ago
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Variational AutoEncoder Tiny Model 🤏

• Host: GitHub
• URL: https://github.com/fer14/tiny_vae
• Owner: Fer14
• Created: 2025-01-20T21:05:59.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-16T00:05:27.000Z (about 1 year ago)
• Last Synced: 2025-04-14T10:07:22.509Z (11 months ago)
• Topics: tiny, vae, variational-autoencoder, visualization
• Language: Python
• Homepage:
• Size: 4.98 MB
• Stars: 5
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Variational AutoEncoder Tiny Model 🤏

![logo](./logos/logo_blue.png)

# Variational AutoEncoder

This project demonstrates the use of a Variational AutoEncoder (VAE) to learn a latent space representation of simple synthetic data: black-and-white images of circles with varying radius, x, and y coordinates. The goal is to map these parameters to a 3-dimensional latent space, where each dimension ideally corresponds to one of the circle's properties.

##  The Data

The dataset consists of black-and-white images of circles, each of shape 32x32, generated using opencv's `cv2.circle `function. Each image contains a single circle, randomly positioned and sized within the frame. The parameters for the circles are:

- `Radius`: Randomly selected within a defined range.

- `X-coordinate`: Random horizontal position of the circle's center.

- `Y-coordinate`: Random vertical position of the circle's center.

Below is a sample visualization of the generated data:

![logo](./plots/data.png)

# The Encoder

![logo](./logos/encoder_blue.png)

The encoder network compresses the flattened 32x32 input image into a 3-dimensional latent representation. It consists entirely of fully connected layers, producing two outputs:

- Mean vector (μ) of the latent distribution.

- Log variance vector (logσ2) of the latent distribution.

# The Decoder

![logo](./logos/decoder_blue.png)

The decoder reconstructs the original image from the 3-dimensional latent representation. It also consists entirely of fully connected layers, ensuring the output matches the flattened dimensions of the original image (32x32).

# The Latent Space

The latent space is a 3-dimensional representation of the circle data. Ideally, each dimension captures a specific property:

![logo](./logos/latent_space_blue.png)

## First dimension

The radius?

![logo](./plots/plot2.png)

## Second dimension

The Y-axis?

![logo](./plots/plot3.png)

## Third dimension

The X-axis?

![logo](./plots/plot1.png)

## Future Work

- [ ]  Add Beta-VAE

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

1. Fork the repository

2. Create your feature branch (`git checkout -b feature/AmazingFeature`)

3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)

4. Push to the branch (`git push origin feature/AmazingFeature`)

5. Open a Pull Request

Made with ❤️ by [Fer14]