Conservative Flows: A New Paradigm of Generative Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1866914542408171520 |
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| author | Gal, Eshed Siddiqui, Md Shahriar Rahim Eliasof, Moshe Haber, Eldad |
| author_facet | Gal, Eshed Siddiqui, Md Shahriar Rahim Eliasof, Moshe Haber, Eldad |
| contents | Modern generative modeling is dominated by transport from a noise prior to data. We propose an alternative paradigm in which generation is performed by a discrete stochastic dynamics that leaves the data distribution invariant, initialized from data-supported states rather than from noise. The framework can utilize any pretrained flow model. We develop two probability-preserving sampling mechanisms, a corrected Langevin dynamics with a Metropolis adjustment and a predictor-corrector flow, that operate directly on existing checkpoints. We validate the framework on a synthetic Swiss-roll target, ImageNet-256 and Oxford Flowers-102, where our samplers consistently improve over the original generation procedures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_06905 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Conservative Flows: A New Paradigm of Generative Models Gal, Eshed Siddiqui, Md Shahriar Rahim Eliasof, Moshe Haber, Eldad Machine Learning Modern generative modeling is dominated by transport from a noise prior to data. We propose an alternative paradigm in which generation is performed by a discrete stochastic dynamics that leaves the data distribution invariant, initialized from data-supported states rather than from noise. The framework can utilize any pretrained flow model. We develop two probability-preserving sampling mechanisms, a corrected Langevin dynamics with a Metropolis adjustment and a predictor-corrector flow, that operate directly on existing checkpoints. We validate the framework on a synthetic Swiss-roll target, ImageNet-256 and Oxford Flowers-102, where our samplers consistently improve over the original generation procedures. |
| title | Conservative Flows: A New Paradigm of Generative Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.06905 |