  Optimal Transport (OT) problem aims to find a transport plan that bridges two
distributions while minimizing a given cost function. OT theory has been widely
utilized in generative modeling. In the beginning, OT distance has been used as
a measure for assessing the distance between data and generated distributions.
Recently, OT transport map between data and prior distributions has been
utilized as a generative model. These OT-based generative models share a
similar adversarial training objective. In this paper, we begin by unifying
these OT-based adversarial methods within a single framework. Then, we
elucidate the role of each component in training dynamics through a
comprehensive analysis of this unified framework. Moreover, we suggest a simple
but novel method that improves the previously best-performing OT-based model.
Intuitively, our approach conducts a gradual refinement of the generated
distribution, progressively aligning it with the data distribution. Our
approach achieves a FID score of 2.51 on CIFAR-10 and 5.99 on CelebA-HQ-256,
outperforming unified OT-based adversarial approaches.
