Analyzing and Improving Optimal-Transport-based Adversarial Networks

• URL: http://arxiv.org/abs/2310.02611v2
• Date: Thu, 7 Mar 2024 05:13:11 GMT
• Title: Analyzing and Improving Optimal-Transport-based Adversarial Networks
• Authors: Jaemoo Choi, Jaewoong Choi, Myungjoo Kang
• Abstract summary: 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. 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.
• Score: 9.980822222343921
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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