On most of the tutorials on GANs that I came across the only monitored quantity is training loss.
1)Are there any general conclusions that could be derived from comparing training and validation losses in a GAN?
2)If the losses on training and validation data have a different pattern, what can the validation loss tell about the performance of a GAN?
3)In ideal case scenario should the adversarial losses have similar pattern on training and validation data?
4)Lets say we have a CycleGAN. The discriminator is basically a classifier. Can we diagnose memorization in a discriminator of a GAN by monitoring its loss on unseen data(validation set)?