https://github.com/billsioros/roughgan
Generating Realistic Nanorough Surfaces Using an N-Gram-Graph Augmented Deep Convolutional Generative Adversarial Network
https://github.com/billsioros/roughgan
Last synced: 7 months ago
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Generating Realistic Nanorough Surfaces Using an N-Gram-Graph Augmented Deep Convolutional Generative Adversarial Network
- Host: GitHub
- URL: https://github.com/billsioros/roughgan
- Owner: billsioros
- License: mit
- Created: 2021-03-01T18:10:33.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2025-03-14T06:04:56.000Z (7 months ago)
- Last Synced: 2025-03-14T07:22:28.430Z (7 months ago)
- Language: Python
- Homepage: https://billsioros.github.io/RoughGAN/
- Size: 2.36 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: docs/CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: docs/CODE_OF_CONDUCT.md
- Codeowners: .github/CODEOWNERS
- Security: .github/SECURITY.md
Awesome Lists containing this project
README
RoughGAN
> Accompanying code for the paper [**Generating Realistic Nanorough Surfaces Using an N-Gram-Graph Augmented Deep Convolutional Generative Adversarial Network**](https://dl.acm.org/doi/fullHtml/10.1145/3549737.3549794) presented at [**SETN 2022**](https://hilab.di.ionio.gr/setn2022/).
In this work, we look at how a Generative Adversarial Network (GAN)-based strategy, given a nanorough surface data set, may learn to produce nanorough surface samples that are statistically equivalent to the ones belonging to the training data set. We also look at how combining the GAN framework with a variety of nanorough similarity measures might improve the realisticity of the synthesized nanorough surfaces. We showcase via multiple experiments that our framework is able to produce sufficiently realistic nanorough surfaces, in many cases indistinguishable from real ones.
> **Attention**: We are in the midst of updating this repository so a few things might not work as expected :fire:
## :cd: Getting started
You can run the model locally using the following commands:
```bash
docker build . -t roughgan:$( git tag -l | tail -1 | cut -c2- ) -t build:train -f Dockerfile
docker run -v $(pwd)/data:/home/app/app/data -v $(pwd)/models:/home/app/app/models --gpus $(nvidia-smi --list-gpus | wc -l) roughgan:latest
```
The project's documentation can be found [here](https://billsioros.github.io/RoughGAN/).
## :heart: Support the project
If you would like to contribute to the project, please go through the [Contributing Guidelines](https://billsioros.github.io/RoughGAN/latest/CONTRIBUTING/) first. You can also support the project by [**Buying me a coffee! ☕**](https://www.buymeacoffee.com/billsioros).
## :bookmark_tabs: Citation
```bibtex
@inproceedings{10.1145/3549737.3549794,
author = {Sioros, Vasilis and Giannakopoulos, George and Constantoudis, Vassileios},
title = {Generating Realistic Nanorough Surfaces Using an N-Gram-Graph Augmented Deep Convolutional Generative Adversarial Network},
year = {2022},
isbn = {9781450395977},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3549737.3549794},
doi = {10.1145/3549737.3549794},
booktitle = {Proceedings of the 12th Hellenic Conference on Artificial Intelligence},
articleno = {53},
numpages = {10},
keywords = {Machine Learning, Rough Surfaces, Graph Theory, Nanotechnology, Artificial Intelligence},
location = {Corfu, Greece},
series = {SETN '22}
}
```
This project was generated using the [`billsioros/cookiecutter-pypackage`](https://github.com/billsioros/cookiecutter-pypackage) cookiecutter template.