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WGAN-GP

IMvision12 commited on Jun 9, 2022

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@@ -12,7 +12,7 @@ Space link : [Demo](https://huggingface.co/spaces/keras-io/WGAN-GP)
 ## Wasserstein GAN (WGAN) with Gradient Penalty (GP)
 Original Paper Of WGAN : [Paper](https://arxiv.org/abs/1701.07875)<br>
-Wasserstein GANs With with Gradient Penalty : [Paper](https://arxiv.org/abs/1704.00028)
 The original Wasserstein GAN leverages the Wasserstein distance to produce a value function that has better theoretical properties than the value function used in the original GAN paper. WGAN requires that the discriminator (aka the critic) lie within the space of 1-Lipschitz functions. The authors proposed the idea of weight clipping to achieve this constraint. Though weight clipping works, it can be a problematic way to enforce 1-Lipschitz constraint and can cause undesirable behavior, e.g. a very deep WGAN discriminator (critic) often fails to converge.

 ## Wasserstein GAN (WGAN) with Gradient Penalty (GP)
 Original Paper Of WGAN : [Paper](https://arxiv.org/abs/1701.07875)<br>
+Wasserstein GANs With Gradient Penalty : [Paper](https://arxiv.org/abs/1704.00028)
 The original Wasserstein GAN leverages the Wasserstein distance to produce a value function that has better theoretical properties than the value function used in the original GAN paper. WGAN requires that the discriminator (aka the critic) lie within the space of 1-Lipschitz functions. The authors proposed the idea of weight clipping to achieve this constraint. Though weight clipping works, it can be a problematic way to enforce 1-Lipschitz constraint and can cause undesirable behavior, e.g. a very deep WGAN discriminator (critic) often fails to converge.