A deep learning framework for geodesics under spherical Wasserstein-Fisher-Rao metric and its application for weighted sample generation. (arXiv:2208.12145v1 [cs.LG])

Wasserstein-Fisher-Rao (WFR) distance is a family of metrics to gauge the
discrepancy of two Radon measures, which takes into account both transportation
and weight change. Spherical WFR distance is a projected version of WFR
distance for probability measures so that the space of Radon measures equipped
with WFR can be viewed as metric cone over the space of probability measures
with spherical WFR. Compared to the case for Wasserstein distance, the
understanding of geodesics under the spherical WFR is less …

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