解决扩散桥模型在目标端的拟合不足问题，提升图像修复与转换效果。
Resolving Endpoint Underfitting in Diffusion Bridges via Noise Alignment
- 用均值网络生成更清晰的条件目标，缓解输入与目标噪声不匹配问题。
- 提出噪声对齐映射，显著改善接近目标时的方差和方向预测偏差。
- 适用于图像修复、翻译等任务，代码已开源，实测效果提升明显。
扩散桥模型为连接两个数据分布（如图像修复与转换）提供了强大框架。现有方法多通过模仿标准扩散模型的得分匹配形式学习该桥接过程，但我们发现，这种做法在接近目标分布（t→0）时会出现异常的欠拟合现象，表现为预测方差和方向存在显著漂移，根源在于网络输入与回归目标间噪声水平差异过大。为解决此问题，我们提出噪声对齐扩散桥（NADB）。该方法先通过均值网络提供更清洁的条件目标，再引入新颖的噪声对齐映射关系，有效缓解噪声不匹配问题，修正目标端的欠拟合。在多个图像修复与图像转换任务上的实验验证了本方法的有效性。代码已公开于 https://github.com/gyr02/NADB。
原文摘要 · Abstract (English)
Diffusion bridge models offer a powerful framework for connecting two data distributions, such as in image restoration and translation. Many existing methods learn this bridge by mimicking the score-matching formulation of standard diffusion models. In this work, we find that this way leads to an anomalous underfitting phenomenon near the target endpoint, as the process approaches the target distribution ($t \to 0$). This underfitting, characterized by significant drift in the predicted variance and direction, results from an excessively large discrepancy in noise levels between the network's input and its regression target.To resolve this issue, we propose the Noise-Aligned Diffusion Bridge (NADB).Our approach reformulates the diffusion bridge by first employing a mean network to provide a cleaner conditional target, and then introducing a novel, noise-aligned mapping relationship. This new formulation resolves the noise mismatch and corrects the underfitting near the target endpoint. Experimental validation across multiple image restoration and image translation tasks demonstrates the effectiveness of our approach. Code is available at https://github.com/gyr02/NADB.
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