42 lines
1.5 KiB
Markdown
42 lines
1.5 KiB
Markdown
# Wasserstein GAN
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This project consisted in studying GANs in the case of Wasserstein distance, as part of the fifth-year course at INSA Toulouse in Applied Mathematics of High Dimensional and Deep Learning.
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Here are the members of our group, listed alphabetically by surname:
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- Paul Corbalan
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- Nicolas Gonel
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- Oihan Joyot
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- Tristan Portugues
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- Florian Zorzynski
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Our project is largely inspired by the following resources, which are the initial article of our project as well as the corresponding code.
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- Article: [[1701.07875] Wasserstein GAN (arxiv.org)](https://arxiv.org/abs/1701.07875)
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- Code: [martinarjovsky/WassersteinGAN (github.com)](https://github.com/martinarjovsky/WassersteinGAN)
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## Installation
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It's important to note that Python 3.11 was used for this project, particularly for compatibility with the PyTorch library, so we recommend using this version.
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1. To install Python 3.11, we recommend using Anaconda, by executing the following command:
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```shell
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conda create -n wasserstein-gan python=3.11
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```
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2. To activate the environment, simply run the following command:
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```shell
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conda activate wasserstein-gan
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```
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3. To install the project's dependencies, simply run the following command:
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```shell
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pip install -r requirements.txt
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```
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## Use
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Details of the experiments are given in the [Jupiter Notebook](./notebook.ipynb). However, they can be reproduced simply by executing the following commands:
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- For training:
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```shell
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python main.py
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```
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- For image generation:
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```shell
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python generate.py
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```
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