One by-product of weighing the candidates by their distance is that the resulting output image is prone to false contours or banding. Increasing reduces this effect at the cost of added granularity or high frequency noise due to the introduction of ever more distant colours to the set. I recommend taking a look at the original paper if you’re interested in learning a bit more about the algorithm[1].
Your training set
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So the argument was never whether we should add nested scopes.
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The MacBook Air got double the storage, but what you might overlook is that it also got double the speed. In theory, the read and write performance should be much faster. In real-world use, you won’t have to wait as long when you’re copying files, importing photos or videos and doing AI-enabled tasks. It could also potentially decrease boot times, meaning your MacBook might be faster at waking up after shutting down, but if so, the improvement may be negligible.