One way to check for overfitting is to compare samples to their nearest neighbors in the training set. Of course that doesn't cover interpolations between two p

One way to check for overfitting is to compare samples to their nearest neighbors in the training set. Of course that doesn't cover interpolations between two points in the training set, but I would argue plausible interpolations are already something "learned". A different problem I see with faces in particular though is that our visual system is actually wired to do some really heavy denoising/pattern matching on faces (for example people seeing the face of Jesus on slices of toast), so the generation of faces doesn't actually need to be that good to produce results that seem appealing to humans.