https://github.com/renan-siqueira/gan-research
This project presents a novel approach to Generative Adversarial Networks (GANs) by employing two generators in competition with each other and against a common discriminator.
https://github.com/renan-siqueira/gan-research
ai-research gan gans generative-adversarial-network neural-networks python pytorch
Last synced: 7 months ago
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This project presents a novel approach to Generative Adversarial Networks (GANs) by employing two generators in competition with each other and against a common discriminator.
- Host: GitHub
- URL: https://github.com/renan-siqueira/gan-research
- Owner: renan-siqueira
- License: mit
- Created: 2023-10-14T13:24:59.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-10-14T18:42:27.000Z (about 2 years ago)
- Last Synced: 2024-01-29T19:32:25.275Z (over 1 year ago)
- Topics: ai-research, gan, gans, generative-adversarial-network, neural-networks, python, pytorch
- Language: Python
- Homepage:
- Size: 8.79 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# GAN Research with Dual Generators
This project presents a novel approach to Generative Adversarial Networks (GANs) by employing two generators in competition with each other and against a common discriminator.
## Overview
Traditional GANs use a single generator and discriminator to learn and generate realistic images. This project explores the use of two generators that both compete against a common discriminator. The aim is to study the performance and characteristics of dual generators in GAN training.
## Features
- Dual Generator Architecture.
- Use of gradient penalty for improved training stability.
- Dynamic directory creation for model checkpoints and generated samples.
- Configurable parameters through a JSON file.
- GPU support detection.## Getting Started
### Prerequisites
- Python 3.x
- PyTorch
- torchvision
- tqdm### Installation
1. Clone the repository.
2. Install the required packages.[*See my other projects for more details about setup and configuration](https://github.com/renan-siqueira/my-own-WGAN-GP-implementation)
### Usage
1. Update the `src/settings/settings.py` with the correct paths.
2. Configure the training parameters in `src/json/params.json`.
3. Execute the training:
```bash
python run.py
```## Structure
- `run.py`: Entry point for training.
- `src/app/training.py`: Contains training-related functions.
- `src/utils/utils.py`: Utility functions.
- `src/json/params.json`: Training parameters in JSON format.
- `src/settings/settings.py`: Path settings.## License
This project is licensed under the MIT License.