this post was submitted on 16 Mar 2026
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How is "upscaling while preserving it" not the exact same philosophy as "enhance by applying a filter?"
You just don't like the specific filter, it's very literally the same process.
Because a pixelated circle being upscaled is a circle, but a pixelated circle being turned into a high definition pie is no longer a circle, and that's especially problematic if the circle was just a cross hair or some other random circle like thing the AI thought was meant to be a pie.
Yes, both things are the same, but that's like saying you had a tiny spider in your house and you were okay because it killed mosquitoes in your house, so you should be okay with having a colony of bats since they are also animals and eat mosquitoes. Yes, both are the same, but the scales and the amount of intrusion are completely different.
If your training data has a pixelated circle as an input and a circle as output, your neural network will "upscale" your pixelated circle to a circle. If your training data has a pixelated circle as input and a high definition pie as output, your neural network will "upscale" your pixelated circle to a high definition pie. Even if it's the same algorithm in both cases.
... How if flying a spaceship different from driving a car? They're both controlled applications of kinetic energy to move people or objects.
At the end of the day, it's all a pile of transistors and the only thing that is of import is the intent behind usage.
In one case it's saying you can use a neural net to take something rendered at resolution A/4 and make it visually indistinguishable from the same render at resolution A.
The other is rendering something and radically changing the artistic or visual style.
Upsampling can be replicated within some margin by lowering framerate and letting the GPU work longer on each frame. It strives to restore detail left out from working quicker by guessing.
You cannot turn this feature off and get similar results by lowering the frame rate. It aims to add detail that was never present by guessing.
Upsampling methods have been produced that don't use neural networks. The differences in behavior are in the realm of efficiency, and in many cases you would be hard pressed to tell which is which. The neural network is an implementation detail.
In the other case, the changes are more broad than can be captured by non AI techniques easily. The generative capabilities are central to the feature.
Process matters, but zooming out too far makes everything identical, and the intent matters too. "I want to see your art better" as opposed to "I want to make your art better".