Neural Optimal Transport with General Cost Functionals

ICLR 2023(2022)

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摘要
We present a novel neural-networks-based algorithm to compute optimal transport (OT) plans and maps for general cost functionals. The algorithm is based on a saddle point reformulation of the OT problem and generalizes prior OT methods for weak and strong cost functionals. As an application, we construct a functional to map data distributions with preserving the class-wise structure of data.
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关键词
Optimal Transport,Neural Networks,Generative Modelling,Unpaired Learning
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