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Learning normalizing flows from Entropy-Kantorovich potentials

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arxiv 2006.06033 v1 pith:B5THF25B submitted 2020-06-10 cs.LG stat.ML

Chris Finlay , Augusto Gerolin , Adam M Oberman , Aram-Alexandre Pooladian This is my paper · ORCID
classification cs.LGstat.ML
keywords normalizingflowsfunctionspotentialcontinuousduallearningscalar
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We approach the problem of learning continuous normalizing flows from a dual perspective motivated by entropy-regularized optimal transport, in which continuous normalizing flows are cast as gradients of scalar potential functions. This formulation allows us to train a dual objective comprised only of the scalar potential functions, and removes the burden of explicitly computing normalizing flows during training. After training, the normalizing flow is easily recovered from the potential functions.

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Cited by 1 Pith paper

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  1. Devil is in the Details: Density Guidance for Detail-Aware Generation with Flow Models

    cs.LG 2025-02 conditional novelty 6.0 of 10

    A new density guidance modification of the generative ODE/SDE lets users set the log-density of samples from flow models, giving a practical dial over image detail.

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