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Of course it does not. The model generates "believable" images not exact ones.

by choeger 5y ago

savolai 5y ago
The point is, it's helpful if the reader can evaluate just how faithful the transformation is vis-a-vis the original.
- amitport 5y ago
  Helpful, interesting, yes. BUT not a real critique on this paper which specifically does not claim anything regarding similarity with the original. This happens a lot with junior peer reviewers, they focus more on what the paper isn't or could be instead of what's actually there.
  - savolai 5y ago
    Sure, if you are evaluating at whether the claims are accurate on that level of precision, similarity may not be relevant. However for the rest of us, evaluating whether the work is relevant in the surrounding world really is important. And for that, resemblance to originals is critical in some use cases. I.e. we're evaluating a fit for specific use cases. Do you believe that is not a useful discussion?
dorkwood 5y ago
This isn't obvious to regular people, though. I can recall seeing a conspiracy get traction on Twitter a few months back, where it was claimed that a photograph of a famous person was actually a body double. Someone used an ML upscaler to "enhance" the image, and their followers began scrutinizing the result: "The teeth are different!", "The nose shape is wrong!", "It's not the same person!"
thrdbndndn 5y ago
There is an app called "face app" or whatever that already did pretty good job upscaling people's face using state-of-the-art AI upscaling. The result is impressive, but the moment you started to use it on someone you're actually familiar, it becomes weird very quickly for the obvious reasons. The teech, for example, are never right. This kind of "believable but not truthful" results are rampant in all these machine-learning based tools. It's not very harmful in case of upscaling a few photos I guess, but I've been bitten by it in an acclaimed translation service called DeepL. I use it to translate Japanese to English frequently, and have found that it often (nontrivially) made up sentences that don't exist in the original paragraphs, sometimes have the opposite meanings, or totally ignore part of the text to make the result "more fluent". And unlike traditional translation tools, they are very hard to notice if you know nothing about the original language. I have to from time to time use some more "primitive" translation tools, and compare the results side by side, to avoid such issues. It's frustrating.
dotancohen 5y ago
Then why is it mentioned that one of the potential uses are in medical imagery? Not to mention what happens when some good-intending but ill-informed agency release an enhanced photo of a suspect on the run.
- dpwm 5y ago
  > Not to mention what happens when some good-intending but ill-informed agency release an enhanced photo of a suspect on the run. The more interesting situation is when such "enhanced photos" are used in prosecutions. I suspect that ML "forensics" techniques and questionable expert witnesses will be used to elevate hunches to guilty verdicts and raise (false) conviction rates.
  - stjohnswarts 5y ago
    I think it would be possible by any defense attorney to show how bad a job it does at reproducing the original by showing hi rez images (actual) vs AI enhance upscales. Shouldn't be hard to demonstrate "reasonable doubt"

Of course it does not. The model generates "believable" images not exact ones.

savolai 5y ago

The point is, it's helpful if the reader can evaluate just how faithful the transformation is vis-a-vis the original.

amitport 5y ago
Helpful, interesting, yes. BUT not a real critique on this paper which specifically does not claim anything regarding similarity with the original. This happens a lot with junior peer reviewers, they focus more on what the paper isn't or could be instead of what's actually there.
- savolai 5y ago
  Sure, if you are evaluating at whether the claims are accurate on that level of precision, similarity may not be relevant. However for the rest of us, evaluating whether the work is relevant in the surrounding world really is important. And for that, resemblance to originals is critical in some use cases. I.e. we're evaluating a fit for specific use cases. Do you believe that is not a useful discussion?

dorkwood 5y ago

This isn't obvious to regular people, though. I can recall seeing a conspiracy get traction on Twitter a few months back, where it was claimed that a photograph of a famous person was actually a body double. Someone used an ML upscaler to "enhance" the image, and their followers began scrutinizing the result: "The teeth are different!", "The nose shape is wrong!", "It's not the same person!"

thrdbndndn 5y ago

There is an app called "face app" or whatever that already did pretty good job upscaling people's face using state-of-the-art AI upscaling. The result is impressive, but the moment you started to use it on someone you're actually familiar, it becomes weird very quickly for the obvious reasons. The teech, for example, are never right. This kind of "believable but not truthful" results are rampant in all these machine-learning based tools. It's not very harmful in case of upscaling a few photos I guess, but I've been bitten by it in an acclaimed translation service called DeepL. I use it to translate Japanese to English frequently, and have found that it often (nontrivially) made up sentences that don't exist in the original paragraphs, sometimes have the opposite meanings, or totally ignore part of the text to make the result "more fluent". And unlike traditional translation tools, they are very hard to notice if you know nothing about the original language. I have to from time to time use some more "primitive" translation tools, and compare the results side by side, to avoid such issues. It's frustrating.

dotancohen 5y ago

Then why is it mentioned that one of the potential uses are in medical imagery? Not to mention what happens when some good-intending but ill-informed agency release an enhanced photo of a suspect on the run.

dpwm 5y ago
> Not to mention what happens when some good-intending but ill-informed agency release an enhanced photo of a suspect on the run. The more interesting situation is when such "enhanced photos" are used in prosecutions. I suspect that ML "forensics" techniques and questionable expert witnesses will be used to elevate hunches to guilty verdicts and raise (false) conviction rates.
- stjohnswarts 5y ago
  I think it would be possible by any defense attorney to show how bad a job it does at reproducing the original by showing hi rez images (actual) vs AI enhance upscales. Shouldn't be hard to demonstrate "reasonable doubt"