genai

"No more fears about bills, no more worrying about being broke or stressing over the monetary concerns"

Sounds like heaven!

Very cool, how did you generate this?

Crikey

YouTube vid of GauGAN

Link: https://www.youtube.com/watch?v=MXWm6w4E5q0

Facts are not copyrightable

Link: http://www.dmlp.org/legal-guide/works-not-covered-copyright#...

If 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-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?

Interesting, also what do you mean by "status of actors"?

With 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?

How 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

But the output result is the same (i.e. human can produce a drawing of a face, GAN can produce a new drawing of a face).

If another animal such as chimp draws a human face what do you think would be the outcome? Would a chimp drawing a face be any different to a human drawing a face?

Good question, IANAL but how is a human artist drawing a face (based on their learned reality of what a face looks like) any different than a GAN drawing a face (based on the GAN learned reality)?

Thanks for sharing

Or use on the cloud, much cheaper https://cloud.google.com/nvidia/