What steps can you take to prevent divergence in training loss when using GANs for video generation

Question

With the help of code, can you tell me What steps you can take to prevent divergence in training loss when using GANs for video generation?

Answer 1

To prevent divergence in training loss when using GANs for video generation, you can take the following steps:

• Use a Wasserstein GAN (WGAN): This stabilizes training by using the Wasserstein loss, which improves the convergence.
• Gradient Penalty: Add a gradient penalty to enforce the Lipschitz constraint for the discriminator.
• Learning Rate Adjustments: Use smaller learning rates, especially for the discriminator.
• Label Smoothing: Apply label smoothing to the discriminator labels to prevent overfitting.
• Use Spectral Normalization: Apply spectral normalization to the discriminator to stabilize training.

Here is the code snippet you can refer to:

In the above code, we are using the following key points:

• WGAN: Using Wasserstein loss helps prevent divergence.
• Gradient Penalty: Enforces the Lipschitz constraint, reducing instability.
• Learning Rate Adjustment: Use lower learning rates to avoid large updates causing instability.
• Label Smoothing: Use label smoothing to reduce the risk of overfitting by the discriminator.

Hence, these steps help stabilize GAN training, reducing the risk of divergence during video generation.