Resolving Endpoint Underfitting in Diffusion Bridges via Noise Alignment
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 Bri...
| Authors | Yurong Gao, Zicheng Zhang, Congying Han et al. |
|---|---|
| Primary category | cs.CV |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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