From 95ee389e31a94a104052bcce0528e3bc6fc783e5 Mon Sep 17 00:00:00 2001 From: Soumith Chintala Date: Thu, 23 Mar 2017 10:47:18 -0400 Subject: [PATCH] Update README.md --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 78f59e3..4325d02 100644 --- a/README.md +++ b/README.md @@ -3,13 +3,13 @@ Wasserstein GAN Code accompanying the paper ["Wasserstein GAN"](https://arxiv.org/abs/1701.07875) -##A few notes +## A few notes - The first time running on the LSUN dataset it can take a long time (up to an hour) to create the dataloader. After the first run a small cache file will be created and the process should take a matter of seconds. The cache is a list of indices in the lmdb database (of LSUN) - The only addition to the code (that we forgot, and will add, on the paper) are the [lines 166-169 of main.py](https://github.com/martinarjovsky/WassersteinGAN/blob/master/main.py#L166-L169). These lines act only on the first 25 generator iterations or very sporadically (once every 500 generator iterations). In such a case, they set the number of iterations on the critic to 100 instead of the default 5. This helps to start with the critic at optimum even in the first iterations. There shouldn't be a major difference in performance, but it can help, especially when visualizing learning curves (since otherwise you'd see the loss going up until the critic is properly trained). This is also why the first 25 iterations take significantly longer than the rest of the training as well. - If your learning curve suddenly takes a big drop take a look at [this](https://github.com/martinarjovsky/WassersteinGAN/issues/2). It's a problem when the critic fails to be close to optimum, and hence it's error stops being a good Wasserstein estimate. Known causes are high learning rates and momentum, and anything that helps the critic get back on track is likely to help with the issue. -##Prerequisites +## Prerequisites - Computer with Linux or OSX - [PyTorch](http://pytorch.org) @@ -17,16 +17,16 @@ Code accompanying the paper ["Wasserstein GAN"](https://arxiv.org/abs/1701.07875 Two main empirical claims: -###Generator sample quality correlates with discriminator loss +### Generator sample quality correlates with discriminator loss ![gensample](imgs/w_combined.png "sample quality correlates with discriminator loss") -###Improved model stability +### Improved model stability ![stability](imgs/compare_dcgan.png "stability") -##Reproducing LSUN experiments +## Reproducing LSUN experiments **With DCGAN:**