The best one can hope for from a NN is that it discerns a model within the training data. There is a way to more-or-less onjectively measure how well it has done this, if at all: if the model requires less information than the data it explains. i.e. fewer bytes. So, "compression algorithms" are a rudimentary model of data; we'd like to do much better than that.

However, NN tend to not be very space-efficient, and also don't usually "explain" the data (in the sense of reproducing it). So this test is hard to apply to them.

BTW: human creativity has much to do with expectation: how obvious it was to you already. So, people with different levels of exposureto some art discipline have different opinions on creativity... and as new styles become known, those opinions change.

Human beings also draw on other fields and experiences, not available in training data. Especially striking, to humans, is inspiration from common experiences that are not recognised as common, as in art that reveals ourselves to us; observational humour. For a computer to use this information, it seems it would need to have human experiences, a body, social interaction etc. Of course, this is a very parochial concept... pure creativity need not be so anthropocentric.