What are the best ways to monitor the training stability of a GAN during coding

Question

Tell me the best ways to monitor the training stability of GAN during coding.

Answer 1

The ways to monitor the training stability for GAN during coding are as follows:

• Loss Monitoring:  You can track generator and discriminator losses over epochs. If the discriminator loss converges to zero, it might be too strong, and if the generator loss is erratic, it may signal instability.

• Inception Score (IS) and Frechet Inception Distance (FID): You can also regularly compute IS and FID on generated images to measure quality and diversity. Lower FID scores indicate more realistic images.

• Generated Sample Visualization: You can save and visualize generated images periodically to assess visual quality and mode collapse (where all outputs look similar).

• Gradients and Weights Check: You can also monitor gradients to detect vanishing or exploding gradients. Layer-wise weight histograms can also reveal imbalances in training.

• Discriminator-Generator Loss Ratio: You can maintain a balanced ratio between generator and discriminator losses, and large discrepancies can indicate one overpowers the other.

Using the above methods, you can monitor the stability of a GAN during coding.