How can spectral normalization be utilized in GANs to stabilize training on noisy datasets

Question

I am facing a problem related to GAN How would you optimize memory efficiency in GANs when training on massive datasets with high-resolution images?

Answer 1

Spectral Normalization can be used in GANs to stabilize training, especially when dealing with noisy datasets, by controlling the Lipschitz constant of the discriminator. Here is the code snippet you can refer to:

• Stabilizes Training: Prevents the discriminator from becoming too strong, which can destabilize GAN training.
• Prevents Overfitting: Helps the model focus on learning the structure of the data instead of fitting to noise.
• Improves Generalization: Encourages better generalization, especially on noisy or complex datasets.

Here is the code snippet you can refer to:

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

• Spectral Normalization: Normalizes weight matrices by their largest singular value to control the discriminator's capacity.
• Stabilizes GAN Training: Helps prevent instability, especially when the dataset is noisy or complex.
• Prevents Mode Collapse: Ensures the discriminator is not too powerful, which can help in mitigating mode collapse in GANs.

Hence, by referring to the above, you can utilize normalization  in GANs to stabilize training on noisy datasets