论文标题
神经最佳运输
Neural Optimal Transport
论文作者
论文摘要
我们提出了一种新型的基于神经网络的算法，以计算最佳的运输图和计划较弱和弱运输成本的计划。为了证明神经网络的使用是合理的，我们证明它们是概率分布之间运输计划的通用近似值。我们在玩具示例和未配对的图像到图像翻译上评估了最佳传输算法的性能。
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.
