How do you handle loss function instability in WGAN-GP during image generation tasks

Question

With the help of Python programming, can you tell me how you handle loss function instability in WGAN-GP during image generation tasks?

Answer 1

To handle loss function instability in WGAN-GP during image generation tasks, you can follow the following key steps:

• Gradient Penalty: Ensure proper implementation of the gradient penalty term to stabilize the discriminator's learning.
• Clipping the Weights (Optional): In the original WGAN, weight clipping is used to enforce the Lipschitz constraint, though WGAN-GP typically avoids this.
• Optimizer Tuning: Use the Adam optimizer with appropriate learning rates for both the discriminator and generator.
• Batch Normalization: Apply batch normalization to the generator and discriminator to stabilize training.
• Monitor Learning Rates: Ensure the learning rates are not too high for stable gradient updates.

Here is the code snippet you can follow:

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

• Gradient Penalty: Stabilizes training by enforcing smooth gradients.
• Optimizer Tuning: Proper learning rates for stable convergence.
• Regularization: Helps prevent overfitting and improves model generalization.

Hence, these adjustments ensure smoother training and to avoid instability in WGAN-GP during image generation tasks.