How can I implement spectral normalization in a GAN to stabilize the training of generated images

Question

Can you tell me how I can implement spectral normalization in a GAN to stabilize the training of generated images?

Answer 1

To implement spectral normalization in a GAN, you normalize the weight matrix of layers by its largest singular value, which stabilizes training by controlling the Lipschitz constant. Here are the steps you can use:

• Install Spectral Normalization: PyTorch provides an easy-to-use API for spectral normalization.
• Apply Spectral Normalization Manually: If needed, you can manually compute and apply spectral normalization:

Hee is the code you can refer to:

In the above code we are using the following:

• Spectral Normalization:

  • Stabilizes GAN training by controlling the Lipschitz constant of the discriminator.
  • Prevents the discriminator from being overly confident.

• Automatic Integration:

  • PyTorch's nn.utils.spectral_norm simplifies the process by wrapping layers directly.

• Manual Control:

  • When fine-tuning is needed, compute and apply normalization iteratively during training.

Hence by using above you can implement spectral normalization in a GAN to stabilize the training of generated images