A Flow Matching Algorithm for Many-Shot Adaptation to Unseen Distributions

• URL: http://arxiv.org/abs/2605.06272v1
• Date: Thu, 07 May 2026 13:47:21 GMT
• Title: A Flow Matching Algorithm for Many-Shot Adaptation to Unseen Distributions
• Abstract summary: FP-FM learns basis functions to span the velocity fields corresponding to a set of training distributions.<n>It adapts to new distributions by computing a simple least-squares projection onto this basis.<n>It achieves greatly improved precision and recall relative to baselines across synthetic and image-based datasets.
• Score: 22.125693648279974
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While generative modeling has achieved remarkable success on tasks like natural language-conditioned image generation, enabling model adaptation from example data points remains a relatively underexplored and challenging problem. To this end, we propose Function Projection for Flow Matching (FP-FM), an algorithm that directly conditions generation on samples from the target distribution. FP-FM learns basis functions to span the velocity fields corresponding to a set of training distributions, and adapts to new distributions by computing a simple least-squares projection onto this basis. This enables efficient generation of samples from diverse target distributions without additional training at inference time. We further introduce multiple variants of FP-FM that provide a trade-off in expressivity and compute by enriching the coefficient calculation, e.g., by making the coefficients dependent on time. FP-FM achieves greatly improved precision and recall relative to baselines across synthetic and image-based datasets, with especially strong gains on unseen distributions.

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