46 lines
1.1 KiB
Markdown
46 lines
1.1 KiB
Markdown
Wasserstein GAN
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===============
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Code accompanying the paper ["Wasserstein GAN"](https://arxiv.org/abs/1701.07875)
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##Prerequisites
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- Computer with Linux or OSX
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- [PyTorch](http://pytorch.org)
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- For training, an NVIDIA GPU is strongly recommended for speed. CPU is supported but training is very slow.
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Two main empirical claims:
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###Generator sample quality correlates with discriminator loss
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![gensample](imgs/w_combined.png "sample quality correlates with discriminator loss")
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###Improved model stability
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![stability](imgs/compare_dcgan.png "stability")
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##Reproducing LSUN experiments
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**With DCGAN:**
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```bash
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python main.py --dataset lsun --dataroot [lsun-train-folder] --cuda
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```
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**With MLP:**
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```bash
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python main.py --mlp_G --ngf 512
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```
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Generated samples will be in the `samples` folder.
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If you plot the value `-Loss_D`, then you can reproduce the curves from the paper. The curves from the paper (as mentioned in the paper) have a median filter applied to them:
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```python
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med_filtered_loss = scipy.signal.medfilt(-Loss_D, dtype='float64'), 101)
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```
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More improved README in the works.
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