mlsanityData to 7 Oct 2026

Analyzing and Improving Optimal-Transport-based Adversarial Networks

arXiv 2310.02611 ↗First posted 4 Oct 2023cs.LG cs.CVcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 53 of its 69 references that are in the map. First posted on arXiv 4 Oct 2023.

Builds on

53 of 69 references.

References · 53

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 2; 2026: 2.

8 papers.