To resolve gradient imbalance when training GANs for image segmentation, you can follow the following:
- Gradient Penalty: Add a gradient penalty to the discriminator loss to ensure the gradients are well-behaved and prevent imbalance.
- Label Smoothing: Apply label smoothing to the discriminator's target labels to make the loss less sensitive to small differences.
- Feature Matching: Use feature matching loss to reduce the reliance on the discriminator's gradients and instead focus on matching feature distributions.
- Use Wasserstein GAN with Gradient Penalty (WGAN-GP): This approach stabilizes training and mitigates gradient issues, especially with imbalanced gradients during segmentation tasks.
Here is the code snippet you can refer to:
In the above code, we are using the following key points:
- WGAN-GP: Introduces a gradient penalty term to stabilize training and avoid gradient imbalance.
- Label Smoothing: Softens the discriminator's labels to improve gradient balance.
- Feature Matching: Reduces the sharp dependence on the discriminator's gradients by focusing on feature similarity.
Hence, these methods help stabilize training and manage gradient imbalance when using GANs for complex tasks like image segmentation.
Related Post: Managing gradient issues in deep generative models like GANs and VAEs
