Also, you could verify by writing unit tests with OpenCV to look for similar sources. Since it's all headshots, it will find matches for sure, but it would also find with human faces.
The neural network is starting from noise, but that's not the only input, it was trained on [0] and I think it's arguable that the NN is "reproducing" the images from its training dataset in some sense.
I think it's a very interesting question, of how can we measure when a neural network is being creative? In fact, creativity is not obvious at all. It's sort of an ill-posed question if you think about it. How can you verify that a network is generating things that are not like what it was trained on, yet are... like what it was trained on?
Are neural networks* forever relegated to the role of copying and interpolation? Do the neural network weights form a kind of database?
* (I don't think this only applies to neural networks, but models in general)
There was one recent work trying to address this [1] but I'm not 100% convinced and I think a lot more work is warranted in this area. A difficulty is that it's not a purely technical problem, but also one of semantics and interpretation. It's one that the "automatic musical accompaniment" community and other digital arts communities have struggled with for decades, and it's not resolved.
How do you know when a machine is being creative? It's not far from the moving goalposts problem of general artificial intelligence. How do you know when a machine is being intelligent, if you can always explain it away by examining the black box?
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.
And likewise, how do you know when a human is being creative? Isn't all art derivative of our training and influences? I believe something like that was an argument by one of the random paint splatter artists: that randomness was the only thing truly creative.
Yep, this is a current area of research for content generation.
I think most current approaches build some transform to a latent space and then compare generated images with their nearest neighbors in the training set. If they're identical then your network just learned to reproduce the dataset.
Yeah, I would have at least ran some kind of similarity search on the output. Without that check it's impossible to know if this is actually doing anything.
