https://github.com/nishant2018/wgan-s-wasserstein-gen.ai-

Wasserstein GAN (WGAN) is a variant of the traditional Generative Adversarial Network (GAN) that aims to improve training stability and address issues like mode collapse.
https://github.com/nishant2018/wgan-s-wasserstein-gen.ai-

generative-adversarial-network genrative-ai wasserstein-distance wasserstein-gan wgan

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Wasserstein GAN (WGAN) is a variant of the traditional Generative Adversarial Network (GAN) that aims to improve training stability and address issues like mode collapse.

• Host: GitHub
• URL: https://github.com/nishant2018/wgan-s-wasserstein-gen.ai-
• Owner: Nishant2018
• Created: 2024-07-06T04:12:47.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-07-06T04:15:26.000Z (4 months ago)
• Last Synced: 2024-07-06T05:42:56.035Z (4 months ago)
• Topics: generative-adversarial-network, genrative-ai, wasserstein-distance, wasserstein-gan, wgan
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/code/endofnight17j03/wgan-s-wasserstein-gen-ai
• Size: 1.8 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
### Understanding Wasserstein GAN (WGAN)

**Wasserstein GAN (WGAN)** is a variant of the traditional Generative Adversarial Network (GAN) that aims to improve training stability and address issues like mode collapse. The key differences and concepts are:

1. **Critic Instead of Discriminator**:

   - Traditional GANs use a discriminator to classify inputs as real or fake.

   - WGAN replaces the discriminator with a critic that scores the "realness" of inputs without using a sigmoid activation function.

2. **Wasserstein Distance**:

   - WGAN minimizes the Wasserstein distance (also known as Earth Mover's distance) between the real and generated data distributions.

   - This distance provides a smoother and more meaningful measure of differences between distributions compared to the Jensen-Shannon divergence used in traditional GANs.

3. **Weight Clipping**:

   - To enforce the Lipschitz constraint required for the Wasserstein distance, the weights of the critic are clipped to a small fixed range (e.g., [-0.01, 0.01]).

   - This ensures that the critic function is 1-Lipschitz continuous.

4. **Training Stability**:

   - WGANs improve training stability and provide a more reliable training process.

   - They address common issues like mode collapse, where the generator produces limited diversity in generated samples.

5. **Gradient Penalty**:

   - An enhancement called WGAN-GP (WGAN with Gradient Penalty) replaces weight clipping with a gradient penalty term.

   - This term penalizes the norm of the gradient of the critic with respect to its input, ensuring the Lipschitz constraint more effectively.

### Advantages of WGAN:

- **Improved Training Dynamics**: More stable and reliable training process compared to traditional GANs.

- **Better Loss Metric**: The loss function correlates with the quality of generated samples, providing a meaningful metric during training.

- **Reduced Mode Collapse**: More diverse and realistic outputs from the generator.

### Summary:

WGAN offers a robust approach to training GANs by introducing the Wasserstein distance, replacing the discriminator with a critic, and employing techniques like weight clipping or gradient penalty to enforce Lipschitz continuity. These improvements result in a more stable training process and higher-quality generated samples.