https://github.com/furk4neg3/building_gan_with_pytorch

This repository contains the implementation of a Generative Adversarial Network (GAN).
https://github.com/furk4neg3/building_gan_with_pytorch

Last synced: 28 days ago
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This repository contains the implementation of a Generative Adversarial Network (GAN).

• Host: GitHub
• URL: https://github.com/furk4neg3/building_gan_with_pytorch
• Owner: furk4neg3
• Created: 2024-11-29T18:48:30.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-11-29T18:53:00.000Z (about 1 month ago)
• Last Synced: 2024-11-29T19:39:54.237Z (about 1 month ago)
• Language: Jupyter Notebook
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Building a Generative Adversarial Network (GAN) with PyTorch  

This repository contains the implementation of a Generative Adversarial Network (GAN). The project leverages PyTorch to build and train a GAN, exploring its architecture and applications in generating synthetic data.

## Objectives  

- Understand the architecture of GANs, including the generator and discriminator networks.  

- Implement a GAN using PyTorch to generate synthetic data.  

- Train and optimize the model through adversarial training techniques.  

- Gain insights into handling common challenges such as mode collapse and unstable training.  

## Key Features  

- **Generator Network**: Designed to generate realistic synthetic samples from random noise.  

- **Discriminator Network**: Trained to differentiate between real and generated samples.  

- **Adversarial Training**: Implements a game-theoretic approach to train both networks.  

- **Hands-On Implementation**: Builds foundational knowledge of GANs using PyTorch.  

## Technologies Used  

- **Python**: Primary programming language for implementation.  

- **PyTorch**: Deep learning framework used for constructing and training the GAN.  

- **Jupyter Notebook**: For documenting and running the code interactively.

## Results

The trained GAN is capable of generating realistic synthetic data by learning the underlying distribution of the dataset used during training. The project demonstrates the effectiveness of adversarial training in unsupervised learning contexts.

## Learning Outcomes

- Gained a strong understanding of GAN fundamentals and their applications.

- Developed skills in implementing and training GANs using PyTorch.

- Learned techniques to address challenges in GAN training, such as mode collapse.

## Acknowledgements

This project was completed as part of the Coursera guided project "Deep Learning with PyTorch: Build a Generative Adversarial Network".