  We study the use of amortized optimization to predict optimal transport (OT)
maps from the input measures, which we call Meta OT. This helps repeatedly
solve similar OT problems between different measures by leveraging the
knowledge and information present from past problems to rapidly predict and
solve new problems. Otherwise, standard methods ignore the knowledge of the
past solutions and suboptimally re-solve each problem from scratch. We
instantiate Meta OT models in discrete and continuous settings between
grayscale images, spherical data, classification labels, and color palettes and
use them to improve the computational time of standard OT solvers. Our source
code is available at http://github.com/facebookresearch/meta-ot

Machine Learning;Artificial Intelligence;Machine Learning