Y-Shaped Generative Flows
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918322363170816 |
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| author | Asadulaev, Arip Semenov, Semyon Shtanchaev, Abduragim Moulines, Eric Karray, Fakhri Takac, Martin |
| author_facet | Asadulaev, Arip Semenov, Semyon Shtanchaev, Abduragim Moulines, Eric Karray, Fakhri Takac, Martin |
| contents | Modern continuous-time generative models typically induce \emph{V-shaped} flows: each sample travels independently along a nearly straight trajectory from the prior to the data. Although effective, this independent movement overlooks the hierarchical structures that exist in real-world data. To address this, we introduce \emph{Y-shaped generative flows}, a framework in which samples travel together along shared pathways before branching off to target-specific endpoints. Our formulation is theoretically justified, yet remains practical, requiring only minimal modifications to standard velocity-driven models. We implement this through a scalable, neural network-based training objective. Experiments on synthetic, image, and biological datasets demonstrate that our method recovers hierarchy-aware structures, improves distributional metrics over strong flow-based baselines, and reaches targets in fewer steps. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_11955 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Y-Shaped Generative Flows Asadulaev, Arip Semenov, Semyon Shtanchaev, Abduragim Moulines, Eric Karray, Fakhri Takac, Martin Machine Learning Artificial Intelligence Modern continuous-time generative models typically induce \emph{V-shaped} flows: each sample travels independently along a nearly straight trajectory from the prior to the data. Although effective, this independent movement overlooks the hierarchical structures that exist in real-world data. To address this, we introduce \emph{Y-shaped generative flows}, a framework in which samples travel together along shared pathways before branching off to target-specific endpoints. Our formulation is theoretically justified, yet remains practical, requiring only minimal modifications to standard velocity-driven models. We implement this through a scalable, neural network-based training objective. Experiments on synthetic, image, and biological datasets demonstrate that our method recovers hierarchy-aware structures, improves distributional metrics over strong flow-based baselines, and reaches targets in fewer steps. |
| title | Y-Shaped Generative Flows |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2510.11955 |