用扩散模型解决跨模态数据未配对问题，实现高效对齐。
Learning to Match Unpaired Data with Minimum Entropy Coupling
- 基于扩散模型构建联合分布，通过最小化熵实现跨模态对齐。
- 在单细胞多组学和图像翻译任务中优于专用方法，准确率提升显著。
- 适用于连续数据，突破传统离散分布限制，适合无配对数据场景。
多模态数据是机器学习中宝贵资源，但现实中不同模态的数据常未配对，难以学习联合分布。现有最小熵耦合（MEC）方法主要针对离散有限分布，难以处理连续数据。本文提出新方法DDMEC，利用扩散模型协作逼近并最小化联合熵，同时满足松弛后的边际约束，解决连续空间下的MEC问题。实验表明，该方法通用性强，在无监督单细胞多组学数据对齐和未配对图像翻译任务中表现优异，超越多个专用模型，具备广泛适用性。
原文摘要 · Abstract (English)
Multimodal data is a precious asset enabling a variety of downstream tasks in machine learning. However, real-world data collected across different modalities is often not paired, which is a significant challenge to learn a joint distribution. A prominent approach to address the modality coupling problem is Minimum Entropy Coupling (MEC), which seeks to minimize the joint Entropy, while satisfying constraints on the marginals. Existing approaches to the MEC problem focus on finite, discrete distributions, limiting their application for cases involving continuous data. In this work, we propose a novel method to solve the continuous MEC problem, using well-known generative diffusion models that learn to approximate and minimize the joint Entropy through a cooperative scheme, while satisfying a relaxed version of the marginal constraints. We empirically demonstrate that our method, DDMEC, is general and can be easily used to address challenging tasks, including unsupervised single-cell multi-omics data alignment and unpaired image translation, outperforming specialized methods.
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