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The weights of a NN are just a compressed representation of the training data, think lossy zip. Rank all generated images by similarity to the training data (e

The weights of a NN are just a compressed representation of the training data, think lossy zip. Rank all generated images by similarity to the training data (etc.) and you can see what's stored. The Shannon-Hartley theorem isnt relevant. A 4GB zip of 100TB text data can exactly reproduce the initial 100TB for some distributions of that initial dataset.

chii 4y ago
if you reproduced an exact image (to the same lossy degree as jpeg) using the NN, then you are violating copyright. But if you reproduced an image whose style matches another copyrighted image (e.g., blah in the style of starry night), then how does that new image (which didn't exist before) violate existing copyright? You cannot copyright a style. The NN containing information which _could_ be used to reconstruct an exact image doesn't itself constitute copyright violation - because the right to use information for training NN is not an exclusive right that the original holder of the training set has. So either a new law has to come into existence, vis a vis the right to use copyrighted works to train a NN, or the current copyright laws should apply (which implies that NN generated images which are not "exact" copies of existing works don't violate copyright).
- int_19h 4y ago
  If a given model can consistently reproduce an exact image given the same input prompt, why shouldn't the model itself be considered a compressed form of that image?
  - chii 4y ago
    so does the number pi constitute a copyright infringement?
    - int_19h 4y ago
      No, since it's not a derived work. And if you can come up with a model that can reproduce images exactly without first getting trained on them, it wouldn't be a derived work, either.
      - chii 4y ago
        > No, since it's not a derived work. but why is the set of numbers in a matrix considered derived? I can trivially derive the number pi. My original point is that just because something contains the information does not imply that it violates copyright.
        int_19h 4y ago
        Because said set of numbers is produced via a training process that has the original as an input, and a different input would produce a different set of numbers. You're correct that merely containing the information would not violate copyright - it's all about how that information was produced.
  - mattnewton 4y ago
    Becuase that new image wasn't in the training set?
    - int_19h 4y ago
      With the examples that we're seeing, the images were in the training set.
      - mattnewton 4y ago
        I'm actually seeing plenty of new images that are in the same style but are different from any of the images in the train set, like wonder woman in front of a mountain that looks like the setting of "frozen".
  - FeepingCreature 4y ago
    Determinism is not a copyright violation. ("If a human artist...")
    - mjburgess 4y ago
      Right, but underneath your premise is a scam, right? A NN has not learnt to paint: it doesn't coordinate its sensory-motor system with its environment through play, it hasn't developed any taste, it does not discern the aesthetic good from the bad, it has no judgement, and so on ad infinitum. A NN is just a kNN with an extra compression step. The way all gradient based "learners" work is to compute distances to pregiven training data. In the case of kNN that data is used exactly, in NN its compressed. There is no intelligence here, there is no learning: it's a trick. It turns out that interpolating a point between prior examples can often look novel and often fool a human observer. This is largely due to how incredibly tolerant to flaws we are in the cases where NNs are used to perform this trick. We go to great lengths to impart intention, fix communicative flaws, etc. and this is exploited by "AI" to make simple crap seem great by having the observer fill-in the details, perceptually. I see it as a kind of proto-schizophrenia that all people have which usually works if we're dealing with a human, but on everything else produces religions. In any case, a NN is just a case of a kNN -- which is capable of fooling people exactly the same way, and clearly violates copyright and is a case of theft of intellectual work to make a product you can sell. Adding compression seems irrelevant.
      - FeepingCreature 4y ago
        I don't think this interpretation of NNs is correct. There's been a few papers purporting to show this, but afair they used a very tortured definition of "interpolation". Stable Diffusion is certainly capable of differentiating good from bad. That's why you can tell it to draw good or draw bad. Not that this point is relevant to my comment. "Play", "taste" and "judgment" can be just as deterministic as a sequence of large matrix operations interspersed with nonlinear layers.
        mjburgess 4y ago

chii 4y ago

if you reproduced an exact image (to the same lossy degree as jpeg) using the NN, then you are violating copyright. But if you reproduced an image whose style matches another copyrighted image (e.g., blah in the style of starry night), then how does that new image (which didn't exist before) violate existing copyright? You cannot copyright a style. The NN containing information which _could_ be used to reconstruct an exact image doesn't itself constitute copyright violation - because the right to use information for training NN is not an exclusive right that the original holder of the training set has. So either a new law has to come into existence, vis a vis the right to use copyrighted works to train a NN, or the current copyright laws should apply (which implies that NN generated images which are not "exact" copies of existing works don't violate copyright).

int_19h 4y ago
If a given model can consistently reproduce an exact image given the same input prompt, why shouldn't the model itself be considered a compressed form of that image?
- chii 4y ago
  so does the number pi constitute a copyright infringement?
  - int_19h 4y ago
    No, since it's not a derived work. And if you can come up with a model that can reproduce images exactly without first getting trained on them, it wouldn't be a derived work, either.
    - chii 4y ago
      > No, since it's not a derived work. but why is the set of numbers in a matrix considered derived? I can trivially derive the number pi. My original point is that just because something contains the information does not imply that it violates copyright.
      - int_19h 4y ago
        Because said set of numbers is produced via a training process that has the original as an input, and a different input would produce a different set of numbers. You're correct that merely containing the information would not violate copyright - it's all about how that information was produced.
- mattnewton 4y ago
  Becuase that new image wasn't in the training set?
  - int_19h 4y ago
    With the examples that we're seeing, the images were in the training set.
    - mattnewton 4y ago
      I'm actually seeing plenty of new images that are in the same style but are different from any of the images in the train set, like wonder woman in front of a mountain that looks like the setting of "frozen".
- FeepingCreature 4y ago
  Determinism is not a copyright violation. ("If a human artist...")
  - mjburgess 4y ago
    Right, but underneath your premise is a scam, right? A NN has not learnt to paint: it doesn't coordinate its sensory-motor system with its environment through play, it hasn't developed any taste, it does not discern the aesthetic good from the bad, it has no judgement, and so on ad infinitum. A NN is just a kNN with an extra compression step. The way all gradient based "learners" work is to compute distances to pregiven training data. In the case of kNN that data is used exactly, in NN its compressed. There is no intelligence here, there is no learning: it's a trick. It turns out that interpolating a point between prior examples can often look novel and often fool a human observer. This is largely due to how incredibly tolerant to flaws we are in the cases where NNs are used to perform this trick. We go to great lengths to impart intention, fix communicative flaws, etc. and this is exploited by "AI" to make simple crap seem great by having the observer fill-in the details, perceptually. I see it as a kind of proto-schizophrenia that all people have which usually works if we're dealing with a human, but on everything else produces religions. In any case, a NN is just a case of a kNN -- which is capable of fooling people exactly the same way, and clearly violates copyright and is a case of theft of intellectual work to make a product you can sell. Adding compression seems irrelevant.
    - FeepingCreature 4y ago
      I don't think this interpretation of NNs is correct. There's been a few papers purporting to show this, but afair they used a very tortured definition of "interpolation". Stable Diffusion is certainly capable of differentiating good from bad. That's why you can tell it to draw good or draw bad. Not that this point is relevant to my comment. "Play", "taste" and "judgment" can be just as deterministic as a sequence of large matrix operations interspersed with nonlinear layers.
      - mjburgess 4y ago