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https://github.com/caogang/wgan-gp
A pytorch implementation of Paper "Improved Training of Wasserstein GANs"
https://github.com/caogang/wgan-gp
pytorch wgan-gp
Last synced: 3 days ago
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A pytorch implementation of Paper "Improved Training of Wasserstein GANs"
- Host: GitHub
- URL: https://github.com/caogang/wgan-gp
- Owner: caogang
- License: mit
- Created: 2017-05-04T15:07:18.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2023-07-18T07:54:22.000Z (over 1 year ago)
- Last Synced: 2024-12-14T19:02:43.206Z (10 days ago)
- Topics: pytorch, wgan-gp
- Language: Python
- Homepage:
- Size: 38.5 MB
- Stars: 1,515
- Watchers: 19
- Forks: 347
- Open Issues: 33
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# WGAN-GP
An pytorch implementation of Paper "Improved Training of Wasserstein GANs".# Prerequisites
Python, NumPy, SciPy, Matplotlib
A recent NVIDIA GPU**A latest master version of Pytorch**
# Progress
- [x] gan_toy.py : Toy datasets (8 Gaussians, 25 Gaussians, Swiss Roll).(**Finished** in 2017.5.8)
- [x] gan_language.py : Character-level language model (Discriminator is using **nn.Conv1d**. Generator is using **nn.Conv1d**. **Finished** in 2017.6.23. Finished in 2017.6.27.)
- [x] gan_mnist.py : MNIST (**Running Results while Finished** in 2017.6.26. Discriminator is using **nn.Conv1d**. Generator is using **nn.Conv1d**.)
- [ ] gan_64x64.py: 64x64 architectures(**Looking forward to your pull request**)
- [x] gan_cifar.py: CIFAR-10(**Great thanks to [robotcator](https://github.com/caogang/wgan-gp/pull/18)**)
# Results
- [Toy Dataset](results/toy/)
Some Sample Result, you can refer to the [results/toy/](results/toy/) folder for **details**.
- **8gaussians 154500 iteration**
![frame1612](imgs/8gaussians_frame1545.jpg)
- **25gaussians 48500 iteration**
![frame485](imgs/25gaussians_frame485.jpg)
- **swissroll 69400 iteration**
![frame694](imgs/swissroll_frame694.jpg)
- [Mnist Dataset](results/mnist/)
Some Sample Result, you can refer to the [results/mnist/](results/mnist/) folder for **details**.
![mnist_samples_91899](imgs/mnist_samples_91899.png)
![mnist_samples_91899](imgs/mnist_samples_92299.png)
![mnist_samples_91899](imgs/mnist_samples_92499.png)
![mnist_samples_199999](imgs/mnist_samples_199999.png)
- Billion Word Language Generation (Using CNN, character-level)
Some Sample Result after 8699 epochs which is detailed in [sample](imgs/lang_samples_8699.txt)
I haven't run enough epochs due to that this is very time-comsuming.
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>
> A fosts of shores forreuid he pe
>
> It whith Crouchy digcloued defor
>
> Pamreutol the rered in Car inson
>
> Nor op to the lecs ficomens o fe
>
> In is a " nored by of the ot can
>
> The onteon I dees this pirder ,
>
> It is Brobes aoracy of " medurn
>
> Rame he reaariod to thim atreast
>
> The stinl who herth of the not t
>
> The witl is f ont UAy Y nalence
>
> It a over , tose sho Leloch Cumm- [Cifar10 Dataset](results/cifar10/)
Some Sample Result, you can refer to the [results/cifar10/](results/cifar10/) folder for **details**.
![mnist_samples_91899](imgs/cifar10_samples_80099.jpg)
# Acknowledge
Based on the implementation [igul222/improved_wgan_training](https://github.com/igul222/improved_wgan_training) and [martinarjovsky/WassersteinGAN](https://github.com/martinarjovsky/WassersteinGAN)