Optimal transport (OT) theory can be informally described using the words of the French
mathematician Gaspard Monge (1746–1818): A worker with a shovel in hand has to move a …

In this paper, we present a novel and principled approach to learn the optimal transport
between two distributions, from samples. Guided by the optimal transport theory, we learn …

This paper presents a novel two-step approach for the fundamental problem of learning an
optimal map from one distribution to another. First, we learn an optimal transport (OT) plan …

Graph neural network has shown impressive ability to capture relations among support
(labeled) and query (unlabeled) instances in a few-shot task. It is a feasible way that features …

Establishing dense correspondences across semantically similar images is a challenging
task. Due to the large intra-class variation and background clutter, two common issues occur …

Local feature matching aims at establishing sparse correspondences between a pair of
images. Recently, detector-free methods present generally better performance but are not …

Optimal transport maps between two probability distributions $\mu $ and $\nu $ on $\R^ d $
have found extensive applications in both machine learning and statistics. In practice, these …

This work introduces an optimal transportation (OT) view of generative adversarial networks
(GANs). Natural datasets have intrinsic patterns, which can be summarized as the manifold …

Optimal mass transport (OMT) theory, the goal of which is to move any irregular 3D object
(ie, the brain) without causing significant distortion, is used to preprocess brain tumor …

Optimal transport is a long‐standing theory that has been studied in depth from both
theoretical and numerical point of views. Starting from the 50s this theory has also found a …