How can I fix the problem of non-convergence in GAN models

Question

With the help of code, can you explain to me how I can fix the problem of non-convergence in GAN models?

Answer 1

To address the issue of non-convergence in GAN models, you can implement the following strategies below:

• Loss Function: Use more stable loss functions like Wasserstein loss with gradient penalty (WGAN-GP).
• Regularization: Apply gradient penalty to improve discriminator performance.
• Learning Rate Tuning: Adjust learning rates separately for the generator and discriminator.
• Batch Normalization: Use in the generator for smoother gradients.
• Label Smoothing: Introduce slight noise to discriminator labels.

Here is the code snippet you can refer to:

In the above code, we are using the following:

• WGAN-GP: Stabilizes GAN training with gradient penalty.
• Separate Training Frequencies: Train the generator less frequently than the discriminator.
• Hyperparameter Tuning: Adjust lambda_gp and learning rates.

Hence, by referring to the above, you can fix the problem of non-convergence in GAN models.