The article discusses the convergence analysis of OT-Flow, a deep generative model. It focuses on establishing convergence results for OT-Flow, reformulating it to optimize the velocity field using neural networks. The content covers the theoretical framework, mathematical principles, and rigorous proofs related to the convergence of OT-Flow to optimal transport problems. It also delves into Monte Carlo approximation and the large data limit in training neural networks for sample generation.
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by Yang Jing,Le... at arxiv.org 03-26-2024
https://arxiv.org/pdf/2403.16208.pdfDeeper Inquiries