Educational "GANs" Implementations

Implementing some of the famous GANs architectures.
Explore the docs »

View Demo · Report Bug · Request Feature

Table of Contents

1. About The Project
   • Overview
   • GAN Architectures Included
   • Built With
2. Usage
3. Contributing
4. License
5. Contact
6. Acknowledgments

About The Project

Overview

This repository contains a collection of Generative Adversarial Network (GAN) architectures implemented in Python using the PyTorch library. These implementations serve as educational resources for those interested in understanding and working with GANs.

GAN Architectures Included

1. Conditional GAN (CGAN):

• Two variations of Conditional GANs are implemented, allowing for conditional image generation based on label information.

2. Deep Convolutional GAN (DCGAN):

• A Deep Convolutional GAN is designed for generating high-quality images. It introduces convolutional layers for improved performance.

3. GAN with MNIST Dataset:

• A basic GAN model is trained on the MNIST dataset to generate handwritten digits.

4. Simple GAN for Number Generation:

• A straightforward GAN architecture for generating numerical data, such as sequences of numbers.

5. InfoGAN (Information Maximizing GAN):

• An InfoGAN model that learns disentangled representations and allows for the control of specific attributes in generated images.

Built With

This research project was powered by a robust set of tools, libraries, and frameworks that facilitated data processing, analysis, and visualization:

• The primary programming language for data manipulation, analysis, and visualization.

• Used for interactive data exploration, analysis, and documentation, providing an intuitive environment for code execution and visualization.

• An open-source deep learning framework for flexible and dynamic neural network development.

• scikit-learn is a versatile machine learning library in Python that offers simple and efficient tools for data analysis and modeling, including classification, regression, clustering, and more.

• NumPy is a fundamental package for scientific computing with Python, providing support for arrays and matrices, as well as a wide range of mathematical functions.

• A versatile data manipulation library for cleaning, processing, and analyzing structured data.

• A powerful statistical data visualization library that enhances the presentation of insights through beautiful and informative plots.

• A widely-used data visualization library that offers extensive customization and control over plot aesthetics.

(back to top)

Usage

• Clone this repository to your local machine if you wish to replicate or build upon the work presented here.

git clone https://github.com/SimplyRamin/GANs.git

• Open the notebook you want to check.

(back to top)

Contributing

Contributions are what makes the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
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

(back to top)

License

Distributed under the MIT License. See LICENSE.txt for more information.

(back to top)

Contact

Ramin F. - @SimplyRamin - ferdos.ramin@gmail.com - Website

Project Link: https://github.com/SimplyRamin/GANs

(back to top)

Acknowledgments

I would like to express my gratitude to the data science community for its constant inspiration and support. This project is a testament to the power of data-driven insights and the endless possibilities they offer in understanding and optimizing social media engagement.

(back to top)