How do I fix the gradient penalty implementation in GANs for stability

Question

Can you help me with a problem that asks how I fix the gradient penalty implementation in GANs for stability?

Answer 1

To stabilize GAN training, You can implement the gradient penalty correctly, ensuring it enforces the 1-Lipschitz constraint. Here are a few steps you can consider before moving:

• Sample random points between real and generated data.
• Compute gradients of the discriminator output w.r.t these interpolated points.
• Enforce the gradient norm to be close to 1.

Here is the code snippet you can refer to:

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

• Ensure lambda_gp (gradient penalty weight) is tuned (typically 10).
• Use stable optimizers like RMSProp or Adam with proper learning rates.
• Monitor the gradient norms during training for debugging.

Hence, by referring to the above you can fix the gradient penalty implementation in GANs for stability