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Tensorflow implementation of "BEGAN: Boundary Equilibrium Generative Adversarial Networks"
| Date | Stars |
|---|---|
| 2026-07-24 | 924 |
| 2026-07-25 | 924 |
| 2026-07-28 | 924 |
| 2026-07-30 | 924 |
| 2026-08-06 | 924 |
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# BEGAN in Tensorflow
Tensorflow implementation of [BEGAN: Boundary Equilibrium Generative Adversarial Networks](https://arxiv.org/abs/1703.10717).
## Requirements
- Python 2.7 or 3.x
- [Pillow](https://pillow.readthedocs.io/en/4.0.x/)
- [tqdm](https://github.com/tqdm/tqdm)
- [requests](https://github.com/kennethreitz/requests) (Only used for downloading CelebA dataset)
- [TensorFlow 1.3.0](https://github.com/tensorflow/tensorflow)
## Usage
First download [CelebA](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) datasets with:
$ apt-get install p7zip-full # ubuntu
$ brew install p7zip # Mac
$ python download.py
or you can use your own dataset by placing images like:
data
└── YOUR_DATASET_NAME
├── xxx.jpg (name doesn't matter)
├── yyy.jpg
└── ...
To train a model:
$ python main.py --dataset=CelebA --use_gpu=True
$ python main.py --dataset=YOUR_DATASET_NAME --use_gpu=True
To test a model (use your `load_path`):
$ python main.py --dataset=CelebA --load_path=CelebA_0405_124806 --use_gpu=True --is_train=False --split valid
## Results
### Generator output (64x64) with `gamma=0.5` after 300k steps
### Generator output (128x128) with `gamma=0.5` after 200k steps
### Interpolation of Generator output (64x64) with `gamma=0.5` after 300k steps
### Interpolation of Generator output (128x128) with `gamma=0.5` after 200k steps
### Interpolation of Discriminator output of real images
## Related works
- [DCGAN-tensorflow](https://github.com/carpedm20/DCGAN-tensorflow)
- [DiscoGAN-pytorch](https://github.com/carpedm20/DiscoGAN-pytorch)
- [simulated-unsupervised-tensorflow](https://github.com/carpedm20/simulated-unsupervised-tensorflow)
## Author
Taehoon Kim / [@carpedm20](http://carpedm20.github.io)
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b8d29049afa9847e, topic:tensorflow
matched fp:b8d29049afa9847e, topic:gan