The article introduces CFM for CNFs, addressing training challenges and improving results in various generative tasks. It discusses OT-CFM for dynamic OT approximation and SB-CFM for Schrödinger bridge inference. Experiments show improved training efficiency and performance in single-cell dynamics, image generation, and unsupervised translation.
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