3 ms·
I’m curious as well. I imagine the definition of “derivative work” might be an interesting sub-problem to resolve while trying to answer the original question.
by mikehollinger 8y ago
I’m curious as well. I imagine the definition of “derivative work” might be an interesting sub-problem to resolve while trying to answer the original question.
- tasdfqwer0897 8y agoSo if you wave your hands enough, it seems like maybe there's an argument to be made that the weights of a trained GAN somehow correspond to a 'compilation' of the training data as it's defined in this doc: https://www.copyright.gov/circs/circ14.pdf https://www.copyright.gov/circs/circ14.pdf
- genai 8y agoWith a compilation, you are able to find the original sources, but with a GAN is it even possible to find the original sources based on an output result alone?
- genai 8y agoHow would you even prove “derivative work”? My understanding is that in proving derivative work you would need to show the original, but in a GAN output which could be made up of 20MM nodes you would not even be able to confirm which 200K image(s) where used in the production of the output result
- tasdfqwer0897 8y agoThis actually might have interesting connections to ideas from differential privacy. Maybe the work is derivative of a particular training image if we can easily predict the presence or absence of that training image given only the trained model?
- genai 8y agoIf you load 50k celebrity images into a tensor of size (500000, 28, 28, 3) and then generate a resulting tensor that results in a (28, 28, 3) tensor where each of the pixel locations is merged to form an average face image (similar to https://www.dailymail.co.uk/femail/article-1355521/Average-female-face-The-Face-Tomorrow-Mike-Mike-project.html https://www.dailymail.co.uk/femail/article-1355521/Average-f... ), then although the trained model contains all the images; I would have thought the output average face image is an entirely new creative work?