Learning Straight Flows by Learning Curved Interpolants
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909554164367360 |
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| author | Shankar, Shiv Geffner, Tomas |
| author_facet | Shankar, Shiv Geffner, Tomas |
| contents | Flow matching models typically use linear interpolants to define the forward/noise addition process. This, together with the independent coupling between noise and target distributions, yields a vector field which is often non-straight. Such curved fields lead to a slow inference/generation process. In this work, we propose to learn flexible (potentially curved) interpolants in order to learn straight vector fields to enable faster generation. We formulate this via a multi-level optimization problem and propose an efficient approximate procedure to solve it. Our framework provides an end-to-end and simulation-free optimization procedure, which can be leveraged to learn straight line generative trajectories. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20719 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Straight Flows by Learning Curved Interpolants Shankar, Shiv Geffner, Tomas Machine Learning Flow matching models typically use linear interpolants to define the forward/noise addition process. This, together with the independent coupling between noise and target distributions, yields a vector field which is often non-straight. Such curved fields lead to a slow inference/generation process. In this work, we propose to learn flexible (potentially curved) interpolants in order to learn straight vector fields to enable faster generation. We formulate this via a multi-level optimization problem and propose an efficient approximate procedure to solve it. Our framework provides an end-to-end and simulation-free optimization procedure, which can be leveraged to learn straight line generative trajectories. |
| title | Learning Straight Flows by Learning Curved Interpolants |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.20719 |