Uncertainty-Aware Distribution-to-Distribution Flow Matching for Scientific Imaging
Distribution-to-distribution generative models support scientific imaging tasks ranging from modeling cellular perturbation responses to translating medical images across conditions. Trustworthy generation requires reliability, or generalization across labs, devices, and experimental conditions, and accountability, or detecting out-of-distribution cases where predictions may be unreliable. We leverage Stochastic Flow Matching (SFM), a marginal-preserving stochastic extension of flow matching for improved generalization under distribution shift. SFM augments deterministic flows with a diffusion term together with a learned score-based drift correction, retaining the learned transport margi...
| Authors | Dongxia Wu, Yuhui Zhang, Serena Yeung-Levy et al. |
|---|---|
| Primary category | cs.LG |
| Published | 23 March 2026 |
| Last updated at source | 20 May 2026 |
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