What methods would you use to mitigate discriminator overpowering the generator in GANs with complex architectures

Question

With the help of code can you tell me What methods would you use to mitigate discriminator overpowering the generator in GANs with complex architectures?

Answer 1

To mitigate discriminator overpowering the generator in GANs with complex architectures, you can use the following code snippet showing how:

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

• Use Gradient Penalty: Apply gradient penalty (e.g., in WGAN-GP) to stabilize training and prevent the discriminator from becoming too strong.
• Adjust Learning Rates: Use a lower learning rate for the discriminator compared to the generator.
• Label Smoothing: Apply label smoothing to reduce confidence in discriminator predictions.
• Add Noise to Discriminator Inputs: Add small random noise to real and fake images to make the discriminator less confident.
• Update Generator More Frequently: Train the generator more frequently than the discriminator to balance their power.

Hence, these techniques help balance the generator and discriminator, improving overall GAN training stability.