
Contextual RNN-GANs for Abstract Reasoning Diagram Generation - MichaelBurge
https://arxiv.org/abs/1609.09444
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MichaelBurge
A dogs classifier can be thought of as training a predicate DOG(X), where X
ranges over image fragments. Is there any research on training n-ary or nested
relations?

I can already train something like OVER(CUP(X), TABLE(X)) as an ordinary
classifier. But OVER() wouldn't generalize to predicates other than CUP or
TABLE, and it couldn't take independent image fragments: OVER(CUP(X),
TABLE(Y)), where X < Z and Y < Z, where < means (rectangular?) subset.

I'd like to train a GAN or similar to generate an image that satisfies
arbitrary formulas in predicate logic, with a dataset of images that are
tagged with formulas. Anyone have any ideas for that?

