> a low-res image of a license plate still contains some data, and that an algorithm can find a license plate number that maximizes the probability of that specific low-res image.
That's because there was enough information (data) present to extrapolate.
Let's say you take a photo of someone across the room, and downsize it so it's low res, then use machine learning to upscale it.
It will do it's best to reconstruct the face/other features based off it's data. It might even get pretty close. But it still has no way of knowing where every single freckle or mole on their skin is - it might try placing some based off what it's learn but they aren't related to the actual person.
Here's another good example [0], it doesn't know what color the bridge should be. Maybe it was painted white, and should stay white! We humans know other information such as which bridge that is, so we know what color it should be, but there's not enough data to extrapolate that from the image alone.
That's because there was enough information (data) present to extrapolate.
Let's say you take a photo of someone across the room, and downsize it so it's low res, then use machine learning to upscale it.
It will do it's best to reconstruct the face/other features based off it's data. It might even get pretty close. But it still has no way of knowing where every single freckle or mole on their skin is - it might try placing some based off what it's learn but they aren't related to the actual person.
Here's another good example [0], it doesn't know what color the bridge should be. Maybe it was painted white, and should stay white! We humans know other information such as which bridge that is, so we know what color it should be, but there's not enough data to extrapolate that from the image alone.
[0] https://camo.githubusercontent.com/3b1aca12e6009a5b8a47bcfbb...