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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.12412 |
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| _version_ | 1866908918098165760 |
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| author | Wang, Tim Y. J. Kuntz, Juan Akyildiz, O. Deniz |
| author_facet | Wang, Tim Y. J. Kuntz, Juan Akyildiz, O. Deniz |
| contents | We introduce a novel particle-based algorithm for end-to-end training of latent diffusion models. We reformulate the training task as minimizing a free energy functional and obtain a gradient flow that does so. By approximating the latter with a system of interacting particles, we obtain the algorithm, which we underpin theoretically by providing error guarantees. The novel algorithm compares favorably in experiments with previous particle-based methods and variational inference analogues. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_12412 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Training Latent Diffusion Models with Interacting Particle Algorithms Wang, Tim Y. J. Kuntz, Juan Akyildiz, O. Deniz Machine Learning We introduce a novel particle-based algorithm for end-to-end training of latent diffusion models. We reformulate the training task as minimizing a free energy functional and obtain a gradient flow that does so. By approximating the latter with a system of interacting particles, we obtain the algorithm, which we underpin theoretically by providing error guarantees. The novel algorithm compares favorably in experiments with previous particle-based methods and variational inference analogues. |
| title | Training Latent Diffusion Models with Interacting Particle Algorithms |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.12412 |