https://github.com/aastopher/potato_gan
Potato Generating DCGAN - a deep dive into GAN basics
https://github.com/aastopher/potato_gan
deep-learning gan gans generative-adversarial-network pytorch
Last synced: 4 months ago
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Potato Generating DCGAN - a deep dive into GAN basics
- Host: GitHub
- URL: https://github.com/aastopher/potato_gan
- Owner: aastopher
- Created: 2022-09-29T19:48:06.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2022-10-26T18:12:01.000Z (over 3 years ago)
- Last Synced: 2025-07-19T20:27:58.619Z (7 months ago)
- Topics: deep-learning, gan, gans, generative-adversarial-network, pytorch
- Language: Jupyter Notebook
- Homepage:
- Size: 108 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Potato Generating - DCGAN (Deep Convolutional Generative Adversarial Network)
The purpose of this project is to introduce the concepts behind basic image generation using a simple implementation of Deep Convolutional Generative Adversarial Networks. This project will detail a basic example, training DCGANs to reproduce images of a single class. Exploring the concepts related to how GANs work in general.
### Running this notebook
* The notebook will check for the presence of the dataset, if the folder does not exist (it will not on first run) then it will download the dataset automatically.
* Given that the pickled models exist, the models will automatically be loaded an usable on your current device pre-trained.
* The notebook will check for the pickled models, if either model does not exists it will begin to re-train on the current device. (i.e. just delete or remove the current model files to re-train)
### References
```
@misc{https://doi.org/10.48550/arxiv.1406.2661,
doi = {10.48550/ARXIV.1406.2661},
url = {https://arxiv.org/abs/1406.2661},
author = {Goodfellow, Ian J. and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
keywords = {Machine Learning (stat.ML), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Generative Adversarial Networks},
publisher = {arXiv},
year = {2014},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
```
@misc{https://doi.org/10.48550/arxiv.1511.06434,
doi = {10.48550/ARXIV.1511.06434},
url = {https://arxiv.org/abs/1511.06434},
author = {Radford, Alec and Metz, Luke and Chintala, Soumith},
keywords = {Machine Learning (cs.LG), Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks},
publisher = {arXiv},
year = {2015},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
```
@misc{https://doi.org/10.48550/arxiv.1606.03498,
doi = {10.48550/ARXIV.1606.03498},
url = {https://arxiv.org/abs/1606.03498},
author = {Salimans, Tim and Goodfellow, Ian and Zaremba, Wojciech and Cheung, Vicki and Radford, Alec and Chen, Xi},
keywords = {Machine Learning (cs.LG), Computer Vision and Pattern Recognition (cs.CV), Neural and Evolutionary Computing (cs.NE), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Improved Techniques for Training GANs},
publisher = {arXiv},
year = {2016},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```