https://github.com/nishant2018/variational-autoencoder-a.encoder-gen.ai

Variational Autoencoders (VAEs) are a type of generative model that extends traditional autoencoders by adding a probabilistic spin to their latent space representation.
https://github.com/nishant2018/variational-autoencoder-a.encoder-gen.ai

autoencoder autoencoders generative-ai variational-autoencoder

Last synced: 3 days ago
JSON representation

Variational Autoencoders (VAEs) are a type of generative model that extends traditional autoencoders by adding a probabilistic spin to their latent space representation.

• Host: GitHub
• URL: https://github.com/nishant2018/variational-autoencoder-a.encoder-gen.ai
• Owner: Nishant2018
• Created: 2024-06-28T08:43:17.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-07-03T10:45:39.000Z (4 months ago)
• Last Synced: 2024-07-03T12:00:30.262Z (4 months ago)
• Topics: autoencoder, autoencoders, generative-ai, variational-autoencoder
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/code/endofnight17j03/variational-autoencoder-a-encoder-gen-ai
• Size: 633 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
Variational Autoencoders (VAEs) are a type of generative model that extends traditional autoencoders by adding a probabilistic spin to their latent space representation. Unlike traditional autoencoders that map input data to a fixed, deterministic latent space, VAEs map data to a distribution in the latent space, typically a Gaussian distribution. This allows VAEs to generate new data points by sampling from this learned distribution, enabling them to generate novel outputs.

Key concepts in VAEs include:

1. **Encoder Network**: Maps input data to the parameters (mean and variance) of the latent distribution.

2. **Latent Space**: Represents data in a lower-dimensional space where each point corresponds to a distribution rather than a single point.

3. **Reparameterization Trick**: Enables efficient backpropagation through the sampling process by decoupling the stochastic part (sampling from the learned distribution) from the deterministic part (parameter calculations).

4. **Decoder Network**: Reconstructs data from samples drawn from the latent space distribution.

VAEs are trained by optimizing a loss function that balances two objectives:

- **Reconstruction Loss**: Measures how accurately the VAE can reconstruct the input data.

- **KL Divergence Loss**: Ensures that the learned latent space distributions match a prior distribution (usually a unit Gaussian), promoting regularization and improving sample generation.

Applications of VAEs include image generation, data compression, and representation learning in unsupervised settings. They are particularly useful for tasks where generating new data based on learned representations is required.