Flow Matching with Uncertainty Quantification and Guidance

• URL: http://arxiv.org/abs/2602.10326v1
• Date: Tue, 10 Feb 2026 22:03:13 GMT
• Title: Flow Matching with Uncertainty Quantification and Guidance
• Authors: Juyeop Han, Lukas Lao Beyer, Sertac Karaman,
• Abstract summary: Uncertainty-aware flow matching (UA-Flow) is a lightweight extension of flow matching that predicts the velocity field together with heteroscedastic uncertainty.<n>UA-Flow estimates per-sample uncertainty by propagating velocity uncertainty through the flow dynamics.<n>Experiments on image generation show that UA-Flow produces uncertainty signals more highly correlated with sample fidelity than baseline methods.
• Score: 14.952056744888912
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the remarkable success of sampling-based generative models such as flow matching, they can still produce samples of inconsistent or degraded quality. To assess sample reliability and generate higher-quality outputs, we propose uncertainty-aware flow matching (UA-Flow), a lightweight extension of flow matching that predicts the velocity field together with heteroscedastic uncertainty. UA-Flow estimates per-sample uncertainty by propagating velocity uncertainty through the flow dynamics. These uncertainty estimates act as a reliability signal for individual samples, and we further use them to steer generation via uncertainty-aware classifier guidance and classifier-free guidance. Experiments on image generation show that UA-Flow produces uncertainty signals more highly correlated with sample fidelity than baseline methods, and that uncertainty-guided sampling further improves generation quality.

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