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Couldn't we still experience some loss of information/aliasing after projecting onto the closest point? Or is this intended to mitigate, not solve, the aliasing problem?


What's an actual situation that we need to repetitively downsample and upsample a model?


Are there any applications that would require downsampling and then upsampling?


if we downsample and then upsample again, why will the result be much different than the original?