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Anyway, check out chapter 5 of my book for details on how DL models manage to do it, and why this is conducive to (local) generalizationhttps://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff …
Manifold interpolation is like asking: what's the mid-point between a fork and a spoon (it terms of their function)? Which you can approximate if you've got a database of utensils related by pairwise functional distance
Taking a bunch of numerical features (e.g. images) describing spoons and forks and interpolating linearly between them would be entirely meaningless...
This is how we end up with sporks. Nobody likes sporks
Seems like defining that manifold or quantifying functionality in any meaningful way is the hard part, no?
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