Interpreting diffusion score matching using normalizing flow

• URL: http://arxiv.org/abs/2107.10072v1
• Date: Wed, 21 Jul 2021 13:27:32 GMT
• Title: Interpreting diffusion score matching using normalizing flow
• Authors: Wenbo Gong, Yingzhen Li
• Abstract summary: We show that diffusion score matching (DSM) (or diffusion Stein discrepancy (DSD)) is equivalent to the original score matching (or Stein discrepancy) evaluated in a normalizing flow. Specifically, we prove that DSM (or DSD) is equivalent to the original score matching (or Stein discrepancy) evaluated in the transformed space defined by the normalizing flow.
• Score: 22.667661526643265
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scoring matching (SM), and its related counterpart, Stein discrepancy (SD) have achieved great success in model training and evaluations. However, recent research shows their limitations when dealing with certain types of distributions. One possible fix is incorporating the original score matching (or Stein discrepancy) with a diffusion matrix, which is called diffusion score matching (DSM) (or diffusion Stein discrepancy (DSD)). However, the lack of interpretation of the diffusion limits its usage within simple distributions and manually chosen matrix. In this work, we plan to fill this gap by interpreting the diffusion matrix using normalizing flows. Specifically, we theoretically prove that DSM (or DSD) is equivalent to the original score matching (or Stein discrepancy) evaluated in the transformed space defined by the normalizing flow, where the diffusion matrix is the inverse of the flow's Jacobian matrix. In addition, we also build its connection to Riemannian manifolds and further extend it to continuous flows, where the change of DSM is characterized by an ODE.

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