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No there certainly is a chance for ML to improve here. Let’s take the classic example of enhancing a blurry photo to get a license plate. Humans may not be ab

No there certainly is a chance for ML to improve here. Let’s take the classic example of enhancing a blurry photo to get a license plate. Humans may not be able to see much in the blur, but an AI trained on many different highly down-res’d images could at least give you plausible outcomes using far less data than a human brain would be able to say anything with confidence. You wouldn’t hold it up as the absolute truth, but you’d run the potential plate and see if it matched some other data you have. So yes, it wouldn’t magically add any more information to the image, but it could be far better at taking low information and giving plausible outcomes that are then necessary to verify.

hojjat12000 6y ago
Actually the opposite. These algorithms are more susceptible to noise, they may generate sharp perfect license plate numbers (that are totally fabricated and completely wrong) from a blurry image. But by no means should you even consider the results to have hints of truth. GAN produces totally different results if you slightly change the input. So, as others are also saying, these "enhances" are great for decoration and absolutely should be ignored as facts or truth (specially when it comes to face and license plate and others used by the law enforcement).
- nwienert 6y ago
  No. If their training set isn’t too far off what you use it for, it is valid. Just because it’s not guaranteed, doesn’t mean it’s not more accurate than hitting sharpen and squinting. You’re fighting against “would it be reliable” but that isn’t the claim. The claim is could it be better than human, and the answer is yes, it just depends on how well trained it is and the dataset. But this is also entirely testable. I guarantee much like Go, if we set up a “human vs AI guess the blurry image” competition that AI will blow us out of the water. It’s simply a data * training issue, and humans don’t spend hours on end practicing enhancing images like they do playing Chess. Again - it won’t be perfect, obviously. It will have false positives, of course. Doesn’t mean it can’t be better than human. Also GANs are pretty irrelevant, the model structure has nothing to do with the theory.
- perl4ever 6y ago
  >These algorithms are more susceptible to noise, they may generate sharp perfect license plate numbers (that are totally fabricated and completely wrong) from a blurry image. This is not really an issue that is new or limited to things that are called AI. https://www.theregister.com/2013/08/06/xerox_copier_flaw_means_dodgy_numbers_and_dangerous_designs/ https://www.theregister.com/2013/08/06/xerox_copier_flaw_mea...
medlyyy 6y ago
That's a fair point. Police use artist sketches from witness descriptions to help identify a suspect. It's a similar idea. The difficulty will be making sure people treat it the same way, because it looks like a normal image.
- webmaven 6y ago
  > The difficulty will be making sure people treat it the same way, because it looks like a normal image. Hmm. Mandating the use of style transfer to make it look like an artist's rendering would probably have the intended effect.
qayxc 6y ago
> Let’s take the classic example of enhancing a blurry photo to get a license plate. That's not the same as fabricating information, though. A blurry image still contains a whole bunch of information and correlation data that just isn't present in a handful of pixels. This is not super-resolution, but something different entirely. Super-resolution would mean to produce a readable license plate from just a handful of pixels. That is an impossible task, since the pixels alone would necessarily match more than one plate. The algorithm would therefore have to "guess" and the result will match something that is has been trained on (read: plausible), but by no means the correct one, no matter how many checks you run on a database. To illustrate the point, I took an image of a random license plate, and scaled it down to 12x6 pixels. 4x super-resolution would bring it to 48x24 pixels and should produce perfectly readable results. Here's how it looks (original, down-scaled to 48x24, and down-scaled to 12x6 pixels): https://pasteboard.co/JSu3WDU.png https://pasteboard.co/JSu3WDU.png The 48x24 pixel version could easily be upscaled to even make the state perfectly readable. A 4x super-resolution upscale of the 12x6 version, however, would be doomed to fail no matter what. That's what I'm getting at. Just for shits and giggles, here's the AI 4x super-resolution result: https://pasteboard.co/JSu7jkP.png https://pasteboard.co/JSu7jkP.png Edit: while I'm having fun with super-resolution, here's the upscaled result from the 48x24 pixel version: https://pasteboard.co/JSu9Qh6.png https://pasteboard.co/JSu9Qh6.png
- nwienert 6y ago
  I was simply pointing out that AI enhancement to find details otherwise not possible by humans that could be useful/accurate is very possible, and I don’t think you refuted it.
  - qayxc 6y ago
    I also never denied that. But there's a difference between finding details that would otherwise go amiss (i.e. in lieu of a microscope revealing features invisible to the unaided eye) and reproducing data that simply isn't there to begin with (as is the core of the "enhance"-trope).
deleted 6y ago
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hojjat12000 6y ago

Actually the opposite. These algorithms are more susceptible to noise, they may generate sharp perfect license plate numbers (that are totally fabricated and completely wrong) from a blurry image. But by no means should you even consider the results to have hints of truth. GAN produces totally different results if you slightly change the input. So, as others are also saying, these "enhances" are great for decoration and absolutely should be ignored as facts or truth (specially when it comes to face and license plate and others used by the law enforcement).

nwienert 6y ago
No. If their training set isn’t too far off what you use it for, it is valid. Just because it’s not guaranteed, doesn’t mean it’s not more accurate than hitting sharpen and squinting. You’re fighting against “would it be reliable” but that isn’t the claim. The claim is could it be better than human, and the answer is yes, it just depends on how well trained it is and the dataset. But this is also entirely testable. I guarantee much like Go, if we set up a “human vs AI guess the blurry image” competition that AI will blow us out of the water. It’s simply a data * training issue, and humans don’t spend hours on end practicing enhancing images like they do playing Chess. Again - it won’t be perfect, obviously. It will have false positives, of course. Doesn’t mean it can’t be better than human. Also GANs are pretty irrelevant, the model structure has nothing to do with the theory.
perl4ever 6y ago
>These algorithms are more susceptible to noise, they may generate sharp perfect license plate numbers (that are totally fabricated and completely wrong) from a blurry image. This is not really an issue that is new or limited to things that are called AI. https://www.theregister.com/2013/08/06/xerox_copier_flaw_means_dodgy_numbers_and_dangerous_designs/ https://www.theregister.com/2013/08/06/xerox_copier_flaw_mea...

medlyyy 6y ago

That's a fair point. Police use artist sketches from witness descriptions to help identify a suspect. It's a similar idea. The difficulty will be making sure people treat it the same way, because it looks like a normal image.

webmaven 6y ago
> The difficulty will be making sure people treat it the same way, because it looks like a normal image. Hmm. Mandating the use of style transfer to make it look like an artist's rendering would probably have the intended effect.

qayxc 6y ago

> Let’s take the classic example of enhancing a blurry photo to get a license plate. That's not the same as fabricating information, though. A blurry image still contains a whole bunch of information and correlation data that just isn't present in a handful of pixels. This is not super-resolution, but something different entirely. Super-resolution would mean to produce a readable license plate from just a handful of pixels. That is an impossible task, since the pixels alone would necessarily match more than one plate. The algorithm would therefore have to "guess" and the result will match something that is has been trained on (read: plausible), but by no means the correct one, no matter how many checks you run on a database. To illustrate the point, I took an image of a random license plate, and scaled it down to 12x6 pixels. 4x super-resolution would bring it to 48x24 pixels and should produce perfectly readable results. Here's how it looks (original, down-scaled to 48x24, and down-scaled to 12x6 pixels): https://pasteboard.co/JSu3WDU.png https://pasteboard.co/JSu3WDU.png The 48x24 pixel version could easily be upscaled to even make the state perfectly readable. A 4x super-resolution upscale of the 12x6 version, however, would be doomed to fail no matter what. That's what I'm getting at. Just for shits and giggles, here's the AI 4x super-resolution result: https://pasteboard.co/JSu7jkP.png https://pasteboard.co/JSu7jkP.png Edit: while I'm having fun with super-resolution, here's the upscaled result from the 48x24 pixel version: https://pasteboard.co/JSu9Qh6.png https://pasteboard.co/JSu9Qh6.png

nwienert 6y ago
I was simply pointing out that AI enhancement to find details otherwise not possible by humans that could be useful/accurate is very possible, and I don’t think you refuted it.
- qayxc 6y ago
  I also never denied that. But there's a difference between finding details that would otherwise go amiss (i.e. in lieu of a microscope revealing features invisible to the unaided eye) and reproducing data that simply isn't there to begin with (as is the core of the "enhance"-trope).

deleted 6y ago

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