How do I balance generator and discriminator losses to avoid issues in GAN training

Question

With the help of code and examples, can you tell me how I balance generator and discriminator losses to avoid issues in GAN training?

Answer 1

To balance generator and discriminator losses during GAN training, you can follow the following steps:

• Adjust Learning Rates: Use different learning rates for the generator and discriminator.
• Discriminator Training Steps: Train the discriminator more or less frequently based on loss trends.
• Add Gradient Penalty: Regularize the discriminator to prevent overpowering.
• Loss Scaling: Scale losses to ensure balanced gradients.

Here is the code snippet you can refer to:

In the above code, we are using the following:

• Learning Rate Adjustment: Use a higher learning rate for the discriminator if it’s too weak.
• Training Frequency: Train the discriminator more if it underperforms or skip steps if it overpowers.
• Regularization: Use gradient penalty (e.g., WGAN-GP) to stabilize discriminator training.

Hence, this ensures the generator and discriminator stay balanced during GAN training.