<p>The most common way to stabilize the training of a WGAN is to replace the <strong>Gradient Clipping</strong> technique that was used in the early W-GAN with <strong>Gradient Penalty (WGAN-GP)</strong>. This technique seems outperform the original WGAN. The paper that describes what GP is can be found here:
<a href="https://arxiv.org/pdf/1704.00028.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1704.00028.pdf</a></p>
<p>Also, If you need any help of how to implement this, You can check a nice repository that I have found here:
<a href="https://github.com/kochlisGit/Keras-GAN" rel="nofollow noreferrer">https://github.com/kochlisGit/Keras-GAN</a></p>
<p>There are also other tricks that You can use to improve the overall quality of your generated images, described in the repository. For example:</p>
<ol>
<li>Add Random <strong>Gaussian Noise</strong> at the inputs of the discriminator that decays over the time.</li>
<li>Random/Adaptive <strong>Data Augmentations</strong></li>
<li>Separate <strong>fake/real batches</strong></li>
</ol>
<p>etc.</p>
Training stability of Wasserstein GANs
<p>I am working on a project with Wasserstein GANs and more specifically with an implementation of the improved version of Wasserstein GANs. I have two theoretical questions about wGANs regarding their stability and training process. Firstly, the result of the loss function notoriously is correlated with the quality of the result of the generated samples <a href="https://lilianweng.github.io/lil-log/2017/08/20/from-GAN-to-WGAN.html" rel="nofollow noreferrer">(that is stated here)</a>. Is there some extra bibliography that supports that argument?</p>

<p>Secondly, during my experimental phase, I noticed that training my architecture using wGANs is much faster than using a simple version of GANs. Is that a common behavior? Is there also some literature analysis about that?</p>

<p>Furthermore, one question about the continuous functions that are guaranteed by using Wasserstein loss. I am having some issues understanding this concept in practice, what it means that the normal GANs loss is not continuous function?</p>