通过分解共享结构实现无配对域对齐，提升图像超分辨率效果
SerpentFlow: Generative Unpaired Domain Alignment via Shared-Structure Decomposition
- 将数据潜空间分解为共享与域特异性成分，分离共性结构
- 自动确定低频/高频分割阈值，生成符合低频模式的伪配对样本
- 适用于无配对场景，尤其适合超分辨率与气候降尺度任务
域对齐旨在学习不同域间数据分布的对应关系。本文关注域间存在共同结构模式但具体表现不同的情况，且在缺乏成对观测时尤为困难。提出生成式框架SerpentFlow（基于共享结构分解的生成域适应），将潜空间数据分解为共享成分与域特异性成分。通过保留共享结构并以随机噪声替换域特异性成分，构建共享表示与目标域样本间的合成训练对，从而启用传统上仅限于配对设置的条件生成模型。应用于超分辨率任务中，共享成分对应低频内容，高频细节代表域特异性差异。利用分类器判定的准则自动确定高低频分割频率，实现数据驱动的自适应分解。通过生成保留低频结构、注入随机高频实现的伪对，学习给定共享表示下的目标域条件分布。采用流匹配作为生成管道，但框架兼容其他条件生成方法。在合成图像、物理过程模拟和气候降尺度任务上的实验表明，该方法能有效重建与底层低频模式一致的高频结构，验证了共享结构分解在无配对域对齐中的有效性。
原文摘要 · Abstract (English)
Domain alignment refers broadly to learning correspondences between data distributions from distinct domains. In this work, we focus on a setting where domains share underlying structural patterns despite differences in their specific realizations. The task is particularly challenging in the absence of paired observations, which removes direct supervision across domains. We introduce a generative framework, called SerpentFlow (SharEd-structuRe decomPosition for gEnerative domaiN adapTation), for unpaired domain alignment. SerpentFlow decomposes data within a latent space into a shared component common to both domains and a domain-specific one. By isolating the shared structure and replacing the domain-specific component with stochastic noise, we construct synthetic training pairs between shared representations and target-domain samples, thereby enabling the use of conditional generative models that are traditionally restricted to paired settings. We apply this approach to super-resolution tasks, where the shared component naturally corresponds to low-frequency content while high-frequency details capture domain-specific variability. The cutoff frequency separating low- and high-frequency components is determined automatically using a classifier-based criterion, ensuring a data-driven and domain-adaptive decomposition. By generating pseudo-pairs that preserve low-frequency structures while injecting stochastic high-frequency realizations, we learn the conditional distribution of the target domain given the shared representation. We implement SerpentFlow using Flow Matching as the generative pipeline, although the framework is compatible with other conditional generative approaches. Experiments on synthetic images, physical process simulations, and a climate downscaling task demonstrate that the method effectively reconstructs high-frequency structures consistent with underlying low-frequency patterns, supporting shared-structure decomposition as an effective strategy for unpaired domain alignment.
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