In this paper, we propose a flow-based method for learning all-to-all transfer maps among conditional distributions, approximating pairwise optimal transport. The proposed method addresses the challenge of handling continuous conditions, which often involve a large set of conditions with sparse empirical observations per condition. We introduce a novel cost function that enables simultaneous learning of optimal transports for all pairs of conditional distributions. Our method is supported by a theoretical guarantee that, in the limit, it converges to pairwise optimal transports among infinite pairs of conditional distributions. The learned transport maps are subsequently used to couple data points in conditional flow matching. We demonstrate the effectiveness of this method on synthetic and benchmark datasets, as well as on chemical datasets where continuous physical properties are defined as conditions.
View on arXiv@article{ikeda2025_2504.03188,
title={ Simultaneous Learning of Optimal Transports for Training All-to-All Flow-Based Condition Transfer Model },
author={ Kotaro Ikeda and Masanori Koyama and Jinzhe Zhang and Kohei Hayashi and Kenji Fukumizu },
journal={arXiv preprint arXiv:2504.03188},
year={ 2025 }
}