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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...
I see your point. But what about symmetry breaking? Would a lower dimension manifold dictate equal probability to spork/fooon at t=0.5 ot would it be biased towards real world sporks probably found mislabeled in the dataset?pic.twitter.com/qUjqCBFykT
Yes, if numbers are just pixels on 2d grid. No, if numbers are multiple levels of abstraction of multimodal inputs (vision, tactile, manipulation, usage, hunger, taste, language, social...)
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