论文标题
内核神经最佳运输
Kernel Neural Optimal Transport
论文作者
论文摘要
我们研究了使用一般最佳运输配方并学习随机运输计划的神经最佳运输（非）算法。我们表明，二次成本较弱的情况不可能会学习并非最佳的假计划。为了解决这个问题，我们引入了内核弱二次成本。我们表明，它们提供了改进的理论保证和实践绩效。我们在未配对的图像到图像翻译任务上不需要内核成本。
We study the Neural Optimal Transport (NOT) algorithm which uses the general optimal transport formulation and learns stochastic transport plans. We show that NOT with the weak quadratic cost might learn fake plans which are not optimal. To resolve this issue, we introduce kernel weak quadratic costs. We show that they provide improved theoretical guarantees and practical performance. We test NOT with kernel costs on the unpaired image-to-image translation task.
