How do I fix model instability when training GANs on high-resolution images

Question

With the help of programming, can you explain How to fix model instability when training GANs on high-resolution images?

Answer 1

To fix model instability when training GANs on high-resolution images, you can follow the following steps:

• Progressive Growing: Start training with lower-resolution images and progressively increase the resolution.
• Use Spectral Normalization: Apply spectral normalization to the discriminator to stabilize training.
• Gradient Penalty: Use a gradient penalty (e.g., WGAN-GP) to stabilize the discriminator.
• Lower Learning Rate: Reduce the learning rate to slow down training dynamics.
• Use Batch Normalization or Instance Normalization: Normalize activations to avoid unstable gradients.

Here is the code snippet you can refer to:

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

• Progressive Growing: Start with lower-resolution images and gradually increase resolution.
• Spectral Normalization: Apply spectral normalization to stabilize the discriminator.
• Gradient Penalty: Use WGAN-GP for better stability.
• Learning Rate: Reduce the learning rate during training to prevent instability.

Hence, this will help stabilize GAN training on high-resolution images by carefully controlling the generator's output and the discriminator's capacity.