How can I fix the tuning of batch normalization layers when training GANs

Question

With the help of the code snippet, can you explain how I can fix the tuning of batch normalization layers when training GANs?

Answer 1

To fix tuning issues with batch normalization (BN) layers in GANs, you can follow the following  steps:

• Train Generators and Discriminators Separately
  • Ensure BN layers in the generator and discriminator are tuned independently.
• Use Different Modes for BN
  • Set model.train() explicitly for training and model.eval() for evaluation.
• Avoid BN in Discriminator
  • Replace BN with other normalization techniques like spectral normalization for stability.
• Track Running Statistics Correctly
  • Ensure BN running statistics are updated during training.
• Use Instance Normalization
  • For better generalization, replace BN with instance normalization in the generator.

Here are the code snippets you can refer to explaining the following steps:

Hence, By properly managing normalization layers, you can improve training stability and model performance in GANs.