通过随机扰动提升分布到分布生成效果，显著改善图像生成质量。
Three Forms of Stochastic Injection for Improved Distribution-to-Distribution Generative Modeling
- 在源分布有限样本下，通过扰动输入和流插值点注入随机性。
- 五项跨生物、医学、天文任务中平均提升9个FID点。
- 降低传输成本，更准确捕捉真实变换效应，适合科学模拟场景。
建模任意数据分布间的变换是基础科学挑战，应用于药物发现与进化模拟等领域。尽管流匹配为此提供自然框架，但现有研究主要聚焦于噪声到数据的设定，而对一般分布到分布设置的应用探索不足。我们发现，在此情况下，当源分布也需从有限样本中学习时，标准流匹配因监督稀疏而失效。为此，我们提出一种简单高效的训练方法，通过扰动源样本和流插值点注入随机性。在五个涵盖生物学、放射学和天文学的成像任务中，该方法显著提升生成质量，平均优于现有基线9 FID点。同时，该方法降低输入与生成样本间的传输成本，更清晰揭示变换的真实影响，使流匹配成为科学中多样分布变换模拟的更实用工具。
原文摘要 · Abstract (English)
Modeling transformations between arbitrary data distributions is a fundamental scientific challenge, arising in applications like drug discovery and evolutionary simulation. While flow matching offers a natural framework for this task, its use has thus far primarily focused on the noise-to-data setting, while its application in the general distribution-to-distribution setting is underexplored. We find that in the latter case, where the source is also a data distribution to be learned from limited samples, standard flow matching fails due to sparse supervision. To address this, we propose a simple and computationally efficient method that injects stochasticity into the training process by perturbing source samples and flow interpolants. On five diverse imaging tasks spanning biology, radiology, and astronomy, our method significantly improves generation quality, outperforming existing baselines by an average of 9 FID points. Our approach also reduces the transport cost between input and generated samples to better highlight the true effect of the transformation, making flow matching a more practical tool for simulating the diverse distribution transformations that arise in science.
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