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Actually the opposite. These algorithms are more susceptible to noise, they may generate sharp perfect license plate numbers (that are totally fabricated and co

by 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...

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...