Neural Optimal Transport
Alexander Andreevich Korotin, Daniil Selikhanovych, Evgeny Vladimirovich Burnaev · arXiv (Cornell University) · 2022
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 translation.