Constrained Generative Modeling with Manually Bridged Diffusion Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916633884229632 |
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| author | Naderiparizi, Saeid Liang, Xiaoxuan Zwartsenberg, Berend Wood, Frank |
| author_facet | Naderiparizi, Saeid Liang, Xiaoxuan Zwartsenberg, Berend Wood, Frank |
| contents | In this paper we describe a novel framework for diffusion-based generative modeling on constrained spaces. In particular, we introduce manual bridges, a framework that expands the kinds of constraints that can be practically used to form so-called diffusion bridges. We develop a mechanism for combining multiple such constraints so that the resulting multiply-constrained model remains a manual bridge that respects all constraints. We also develop a mechanism for training a diffusion model that respects such multiple constraints while also adapting it to match a data distribution. We develop and extend theory demonstrating the mathematical validity of our mechanisms. Additionally, we demonstrate our mechanism in constrained generative modeling tasks, highlighting a particular high-value application in modeling trajectory initializations for path planning and control in autonomous vehicles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_20371 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Constrained Generative Modeling with Manually Bridged Diffusion Models Naderiparizi, Saeid Liang, Xiaoxuan Zwartsenberg, Berend Wood, Frank Machine Learning In this paper we describe a novel framework for diffusion-based generative modeling on constrained spaces. In particular, we introduce manual bridges, a framework that expands the kinds of constraints that can be practically used to form so-called diffusion bridges. We develop a mechanism for combining multiple such constraints so that the resulting multiply-constrained model remains a manual bridge that respects all constraints. We also develop a mechanism for training a diffusion model that respects such multiple constraints while also adapting it to match a data distribution. We develop and extend theory demonstrating the mathematical validity of our mechanisms. Additionally, we demonstrate our mechanism in constrained generative modeling tasks, highlighting a particular high-value application in modeling trajectory initializations for path planning and control in autonomous vehicles. |
| title | Constrained Generative Modeling with Manually Bridged Diffusion Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.20371 |