  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.
