Neural Optimal Transport

• URL: http://arxiv.org/abs/2201.12220v1
• Date: Fri, 28 Jan 2022 16:24:13 GMT
• Title: Neural Optimal Transport
• Authors: Alexander Korotin, Daniil Selikhanovych, Evgeny Burnaev
• Abstract summary: We present a novel neural-networks-based algorithm to compute optimal transport maps and plans for strong and weak transport costs. We prove that neural networks are universal approximators of transport plans between probability distributions.
• Score: 82.2689844201373
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel neural-networks-based algorithm to compute optimal transport maps and plans for strong and weak transport costs. To justify the usage of neural networks, we prove that they are universal approximators of transport plans between probability distributions. We evaluate the performance of our optimal transport algorithm on toy examples and on the unpaired image-to-image style translation task.

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