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What's your explanation for the compositional generalization that DALL·E does? E.g. there don't seem to be any baby lemurs in pajamas with calculators in the training data, yet DALL·E generates them perfectly:https://twitter.com/Plinz/status/1347280122202624001 …
That is exactly what manifold interpolation is. E.g. producing a spork by interpolating between a spoon and a fork on a manifold of kitchenware. Interpolation obviously does generalize to items not present in the training set -- DL would be useless otherwise.
The above is basically a fancy version of the "smile vector" or "sunglasses vector" people were demonstrating in 2016. It's interpolation on a visual manifold that was densely sampled during training. It's actually a fantastic demo of what manifold interpolation means!
The easy thing you could do demonstrate manifoldness here is to sample a grid of generated pictures that densely cover the spectrum "character type" X "object held". It would look awesome and I'd love to see it.
Wow. You go, Google
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