Neural Optimal Transport with General Cost Functionals

05/30/2022

∙

by   Arip Asadulaev, et al.

∙

0

∙

share

We present a novel neural-networks-based algorithm to compute optimal transport (OT) plans and maps for general cost functionals. The algorithm is based on a saddle point reformulation of the OT problem and generalizes prior OT methods for weak and strong cost functionals. As an application, we construct a functional to map data distributions with preserving the class-wise structure of data.