In the context of GANs I see many papers designing new discriminator networks.
I'm curious about the usefulness of designing discriminators as modified versions of mainstream models like Inception, MobileNet, EfficientNet etc. My intuition is that the mentioned image classification models are way more refined and standardised than a custom discriminator for a specific paper. In addition, I think their pretrained weights should be helpful.
And if they aren't useful, I'm curious why not. Any links about this topic are much appreciated.