Invertible Generative Modeling using Linear Rational Splines

01/15/2020 ∙ by Hadi M. Dolatabadi, et al. ∙ 34

Normalizing flows attempt to model an arbitrary probability distribution through a set of invertible mappings. These transformations are required to achieve a tractable Jacobian determinant that can be used in high-dimensional scenarios. The first normalizing flow designs used coupling layer mappings built upon affine transformations. The significant advantage of such models is their easy-to-compute inverse. Nevertheless, making use of affine transformations may limit the expressiveness of such models. Recently, invertible piecewise polynomial functions as a replacement for affine transformations have attracted attention. However, these methods require solving a polynomial equation to calculate their inverse. In this paper, we explore using linear rational splines as a replacement for affine transformations used in coupling layers. Besides having a straightforward inverse, inference and generation have similar cost and architecture in this method. Moreover, simulation results demonstrate the competitiveness of this approach's performance compared to existing methods.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 7

page 12

page 14

page 15

Code Repositories

LRS_NF

[AISTATS2020] The official repository of "Invertible Generative Modling using Linear Rational Splines (LRS)".


view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.