Kernel Neural Optimal Transport
2022-05-30Code Available2· sign in to hype
Alexander Korotin, Daniil Selikhanovych, Evgeny Burnaev
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/iamalexkorotin/neuraloptimaltransportOfficialIn paperpytorch★ 228
- github.com/iamalexkorotin/KernelNeuralOptimalTransportOfficialpytorch★ 34
Abstract
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.