How do I avoid mode dropping when training a conditional GAN for high-resolution images

Question

Can you give me examples of how to avoid mode dropping when training a conditional GAN for high-resolution images?

Answer 1

In order to avoid mode dropping when training a conditional GAN for high-resolution images, you can refer to the following steps below:

• Improve Network Capacity: Use deeper architectures for the generator and discriminator.
• Feature Matching Loss: Encourage diversity by comparing features between real and generated images.
• Label Smoothing: Smooth real labels to prevent overconfidence in the discriminator.
• Progressive Training: Start with low resolution and gradually increase the image resolution.
• Regularization: Apply techniques like gradient penalty or spectral normalization for stable training.

Here is the code snippet for you reference:

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

• Network Capacity: Use deeper architectures for both generator and discriminator.
• Feature Matching Loss: Helps reduce mode collapse by encouraging diversity.
• Label Smoothing: Prevents discriminator from being overly confident, improving generator stability.
• Progressive Training: Gradually scale up image resolution to handle high-resolution images effectively.

Hence, these techniques help stabilize training and reduce mode dropping.