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Variational Autoencoder Implementation

This repository contains an implementation of a Variational Autoencoder (VAE) using Python and PyTorch. The project explores how VAEs can be used for dimensionality reduction, data generation, and representation learning.

Project Files

  • variational-autoencoder-implementation.ipynb: The Jupyter Notebook with the code for training and evaluating the VAE.
  • VAE_project_report.pdf: A detailed report explaining the theory behind Variational Autoencoders, the approach used, results, and conclusions.

Overview

  • The notebook includes data preprocessing, model training, and results visualization.
  • The report provides an in-depth explanation of the methodology, including loss functions like the Kullback-Leibler Divergence and Reconstruction Loss.

How to Run

  1. Clone this repository: 
    git clone https://github.com/NeginHeidarifard/variational-autoencoder-implementation.git