How do you troubleshoot unbalanced training loss in a GAN where the discriminator becomes too strong

Question

Can you tell me how you troubleshoot unbalanced training loss in a GAN where the discriminator becomes too strong?

Answer 1

To troubleshoot unbalanced training loss in a GAN where the discriminator becomes too strong, you can follow the following steps:

• Lower the Discriminator's Capacity: Reduce the depth or number of layers in the discriminator.
• Label Smoothing: Apply label smoothing to the real labels, e.g., using values like 0.9 instead of 1.0.
• Train Generator More Frequently: Update the generator multiple times per discriminator update.
• Gradient Penalty: Add a gradient penalty (e.g., WGAN-GP) to stabilize discriminator updates.

Here is the code snippet you can refer to:

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

• Label Smoothing: Reduces the discriminator's overconfidence.
• Balanced Training: Updates the generator more frequently to catch up with the discriminator.
• Reduced Discriminator Complexity: Ensures the discriminator doesn't overpower the generator.

Hence, these steps help balance GAN training and prevent the discriminator from becoming too strong.

Related Post: Techniques to reduce low-quality GAN samples in early training