🧠 Variational Autoencoder (VAE) for Face Generation
This repository contains the implementation of a Variational Autoencoder (VAE) using TensorFlow and Keras. The model is trained on grayscale face images from the Labeled Faces in the Wild (LFW) dataset to learn a compressed latent representation of facial features and generate new, realistic face images from this latent space.
What is a Variational Autoencoder (VAE)?
A Variational Autoencoder (VAE) is a type of generative neural network that learns a probabilistic representation of input data. Unlike a standard autoencoder, which maps an input to a single point in a latent space, a VAE maps the input to a distribution (specifically, a mean and a variance) in the latent space.
This probabilistic approach has two key benefits:
- Continuous Latent Space: It encourages the latent space to be continuous, meaning that a point sampled from any location will likely correspond to a valid output. This makes it ideal for interpolation and generation.
- Reparameterization Trick: By sampling from the learned distribution using a clever technique called the reparameterization trick, the model can backpropagate through the sampling process, allowing it to be trained with gradient descent.
The VAE's objective is to minimize a loss function that combines two components:
- Reconstruction Loss: Ensures the generated image is similar to the original input.
- KL Divergence Loss: Forces the learned latent distributions to be close to a standard normal distribution, which helps in regularization and smooth generation.
🚀 Project Overview
The project is structured into two main components:
- Training Notebook: A Jupyter notebook (
VAE_face.ipynb) that handles the entire training process from data loading and preprocessing to model definition, training, and saving the weights.
- Gradio Application: A second Jupyter notebook (
VAE_Gradio_app.ipynb) that provides a simple web interface to load the trained model and generate new face images interactively.
This setup allows you to not only understand how the VAE works but also to easily demonstrate its generative capabilities.
🎓 How to Train the Model
The training process is defined in the VAE_face.ipynb notebook located in the code/ folder. This notebook walks you through the entire training pipeline.
Prerequisites:
Before you begin, ensure you have the necessary libraries installed. You can install them using pip:
1pip install tensorflow keras matplotlib numpy scikit-learn
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5### 🚀 Live Demo
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7You can try out the VAE face generator and see it in action without any setup! Click the link below to run the Gradio app directly in your browser.
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9**[Try the VAE Face Generator Here!](https://6602cb9b8a62377891.gradio.live)**
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