# Entropy-regularized Optimal Transport Generative Models

arXiv: https://arxiv.org/abs/1811.06763 · HTML: https://mlsanity.com/paper/1811.06763/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2018.

## Built on by (most cited first, 1 of 1)

- [On the potential benefits of entropic regularization for smoothing Wasserstein estimators](https://mlsanity.com/paper/2210.06934/) (2022)

## Abstract (arXiv)

We investigate the use of entropy-regularized optimal transport (EOT) cost in developing generative models to learn implicit distributions. Two generative models are proposed. One uses EOT cost directly in an one-shot optimization problem and the other uses EOT cost iteratively in an adversarial game. The proposed generative models show improved performance over contemporary models for image generation on MNSIT.