CycleGAN is capable of learning a one-to-one mapping between two data distributions without paired examples, achieving the task of unsupervised data translation. However, there is no theoretical guarantee on the property of the learned one-to-one mapping in CycleGAN. In this paper, we experimentally find that, under some circumstances, the one-to-one mapping learned by CycleGAN is just a random one within the large feasible solution space. Based on this observation, we explore to add extra constraints such that the one-to-one mapping is controllable and satisfies more properties related to specific tasks. We propose to solve an optimal transport mapping restrained by a task-specific cost function that reflects the desired properties, and use the barycenters of optimal transport mapping to serve as references for CycleGAN. Our experiments indicate that the proposed algorithm is capable of learning a one-to-one mapping with the desired properties.

本文提出了一种控制 CycleGAN 学习到具有特定任务所需属性的一对一映射的方法 —— 通过一种任务特定成本函数约束优化传输映射，再用最佳传输映射的重心作为 CycleGAN 的参考，实验结果表明该算法能够实现预期的学习效果。

通过最优输运指导 CycleGAN 中的一对一映射