However, it is not a standard part of artist training to obtain and redistribute, without license, the (in this case millions) of paintings they studied.

> Never to the best of my knowledge has this been used to argue that a picture with no visible elements of another infringes.

One only needs to look as far back as 2013, in Williams v. Bridgeport Music, to find such a thing not merely argued, but successfully litigated. In this case, the estate of Marvin Gaye alleged that Robin Thicke's "Blurred Lines" copied the 'feel' and 'sound' of "Got to Give It Up" despite containing no samples or even an identical chord progression.

Perhaps more surprising to you will be the fact that the court found in favor of Gaye's estate, i.e. that "Blurred Lines" was infringing!

A not-insubstantial factor in reaching this decision was, as I alluded to above, the attitude of the defendant regarding the infringement. Thicke testified "No" when asked if he considered himself an honest person, and admitted that "Got to Give It Up" was a direct inspiration for the song.

It would be difficult to argue that the data used to create your ML was anything BUT its explicit inspiration, and as I've mentioned in other posts, this is compounded by the fact that the acquiring the initial dataset is itself a separate and very clear-cut case of copyright infringement.

In the interest of good discourse, do note that many legal scholars and industry experts were, admittedly, shocked by the decision and decry it as fundamentally mistaken. Nevertheless it is now certainly precedential caselaw.

It would be quite interesting to compare the generated images with the dataset using a tineye-style image matcher. I wouldn't be surprised if large segments of the generated pictures are outright identical to some image from the dataset.