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<h1><a href="https://nn.labml.ai/gan/wasserstein/gradient_penalty/index.html">Gradient Penalty for Wasserstein GAN (WGAN-GP)</a></h1>
<p>This is an implementation of
<a href="https://papers.labml.ai/paper/1704.00028">Improved Training of Wasserstein GANs</a>.</p>
<p><a href="https://nn.labml.ai/gan/wasserstein/index.html">WGAN</a> suggests
clipping weights to enforce Lipschitz constraint
on the discriminator network (critic).
This and other weight constraints like L2 norm clipping, weight normalization,
L1, L2 weight decay have problems:</p>
<ol>
<li>Limiting the capacity of the discriminator</li>
<li>Exploding and vanishing gradients (without <a href="https://nn.labml.ai/normalization/batch_norm/index.html">Batch Normalization</a>).</li>
</ol>
<p>The paper <a href="https://papers.labml.ai/paper/1704.00028">Improved Training of Wasserstein GANs</a>
proposal a better way to improve Lipschitz constraint, a gradient penalty.</p>
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