论文标题
元最佳运输
Meta Optimal Transport
论文作者
论文摘要
我们研究了摊销优化来预测输入度量的最佳运输（OT）图，我们称之为元。通过利用过去问题的知识和信息来快速预测和解决新问题，这有助于反复解决不同措施之间的类似OT问题。否则，标准方法忽略了过去解决方案的知识，而是从头开始重新解决每个问题。我们在灰度图像，球形数据，分类标签和调色板之间在离散和连续设置中实例化元模型，并利用它们来改善标准OT求解器的计算时间。我们的源代码可在http://github.com/facebookresearch/meta-ot上获得
We study the use of amortized optimization to predict optimal transport (OT) maps from the input measures, which we call Meta OT. This helps repeatedly solve similar OT problems between different measures by leveraging the knowledge and information present from past problems to rapidly predict and solve new problems. Otherwise, standard methods ignore the knowledge of the past solutions and suboptimally re-solve each problem from scratch. We instantiate Meta OT models in discrete and continuous settings between grayscale images, spherical data, classification labels, and color palettes and use them to improve the computational time of standard OT solvers. Our source code is available at http://github.com/facebookresearch/meta-ot
