Abstract:Natural data observed in R n is often constrained to an m-dimensional manifold M, where m < n. Current generative models represent this manifold by mapping an m-dimensional latent variable through a neural network f θ : R m → R n . Such procedures, which we call pushforward models, incur a straightforward limitation: manifolds cannot in general be represented with a single parameterization, meaning that attempts to do so will incur either computational instability or the inability to learn probability densitie… Show more
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