
## Generative Adversarial Network

This repo contains the model and the notebook to this [this Keras example on WGAN](https://keras.io/examples/generative/wgan_gp/).<br>
Full credits to: [A_K_Nain](https://twitter.com/A_K_Nain)<br>
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 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.

The WGAN-GP method proposes an alternative to weight clipping to ensure smooth training. Instead of clipping the weights, the authors proposed a "gradient penalty" by adding a loss term that keeps the L2 norm of the discriminator gradients close to 1.

<details>
<summary>View Model Summary</summary>

![Generator](./gen.PNG)
![Discriminator](./disc.PNG)

</details>
{"library_name": "keras", "tags": ["GAN"]}
keras tensorboard GAN arxiv:1701.07875 arxiv:1704.00028 has_space region:us
TAGS
#keras #tensorboard #GAN #arxiv-1701.07875 #arxiv-1704.00028 #has_space #region-us 


## Generative Adversarial Network

This repo contains the model and the notebook to this this Keras example on WGAN.<br>
Full credits to: A_K_Nain<br>
Space link : Demo
## Wasserstein GAN (WGAN) with Gradient Penalty (GP)

Original Paper Of WGAN : Paper<br>
Wasserstein GANs With Gradient Penalty : Paper

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.

The WGAN-GP method proposes an alternative to weight clipping to ensure smooth training. Instead of clipping the weights, the authors proposed a "gradient penalty" by adding a loss term that keeps the L2 norm of the discriminator gradients close to 1.

<details>
<summary>View Model Summary</summary>

!Generator
!Discriminator

</details>
## Generative Adversarial Network

This repo contains the model and the notebook to this this Keras example on WGAN.<br>
Full credits to: A_K_Nain<br>
Space link : Demo ## Wasserstein GAN (WGAN) with Gradient Penalty (GP)

Original Paper Of WGAN : Paper<br>
Wasserstein GANs With Gradient Penalty : Paper

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.

The WGAN-GP method proposes an alternative to weight clipping to ensure smooth training. Instead of clipping the weights, the authors proposed a "gradient penalty" by adding a loss term that keeps the L2 norm of the discriminator gradients close to 1.

<details>
<summary>View Model Summary</summary>

!Generator
!Discriminator

</details>
TAGS
#keras #tensorboard #GAN #arxiv-1701.07875 #arxiv-1704.00028 #has_space #region-us 
 ## Generative Adversarial Network

This repo contains the model and the notebook to this this Keras example on WGAN.<br>
Full credits to: A_K_Nain<br>
Space link : Demo ## Wasserstein GAN (WGAN) with Gradient Penalty (GP)

Original Paper Of WGAN : Paper<br>
Wasserstein GANs With Gradient Penalty : Paper

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.

The WGAN-GP method proposes an alternative to weight clipping to ensure smooth training. Instead of clipping the weights, the authors proposed a "gradient penalty" by adding a loss term that keeps the L2 norm of the discriminator gradients close to 1.

<details>
<summary>View Model Summary</summary>

!Generator
!Discriminator

</details>
passage: TAGS
#keras #tensorboard #GAN #arxiv-1701.07875 #arxiv-1704.00028 #has_space #region-us 
## Generative Adversarial Network

This repo contains the model and the notebook to this this Keras example on WGAN.<br>
Full credits to: A_K_Nain<br>
Space link : Demo## Wasserstein GAN (WGAN) with Gradient Penalty (GP)

Original Paper Of WGAN : Paper<br>
Wasserstein GANs With Gradient Penalty : Paper

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.

The WGAN-GP method proposes an alternative to weight clipping to ensure smooth training. Instead of clipping the weights, the authors proposed a "gradient penalty" by adding a loss term that keeps the L2 norm of the discriminator gradients close to 1.

<details>
<summary>View Model Summary</summary>

!Generator
!Discriminator

</details>