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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.06272 [cs.LG] |
| (or arXiv:2605.06272v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.06272 arXiv-issued DOI via DataCite (pending registration) |
From: Tyler Ingebrand [view email]
[v1]
Thu, 7 May 2026 13:47:21 UTC (2,502 KB)
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