Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.14161 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911101733568512 |
|---|---|
| author | Lee, Dohoon Park, Jaehyun Kim, Hyunwoo J. Lee, Kyogu |
| author_facet | Lee, Dohoon Park, Jaehyun Kim, Hyunwoo J. Lee, Kyogu |
| contents | Flow and diffusion models have demonstrated strong performance and training stability across various tasks but lack two critical properties of simulation-based methods: freedom of dimensionality and adaptability to different inference trajectories. To address this limitation, we propose the Multidimensional Adaptive Coefficient (MAC), a plug-in module for flow and diffusion models that extends conventional unidimensional coefficients to multidimensional ones and enables inference trajectory-wise adaptation. MAC is trained via simulation-based feedback through adversarial refinement. Empirical results across diverse frameworks and datasets demonstrate that MAC enhances generative quality with high training efficiency. Consequently, our work offers a new perspective on inference trajectory optimality, encouraging future research to move beyond vector field design and to leverage training-efficient, simulation-based optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_14161 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multidimensional Adaptive Coefficient for Inference Trajectory Optimization in Flow and Diffusion Lee, Dohoon Park, Jaehyun Kim, Hyunwoo J. Lee, Kyogu Machine Learning Artificial Intelligence I.2.6; I.5.1; F.1.1 Flow and diffusion models have demonstrated strong performance and training stability across various tasks but lack two critical properties of simulation-based methods: freedom of dimensionality and adaptability to different inference trajectories. To address this limitation, we propose the Multidimensional Adaptive Coefficient (MAC), a plug-in module for flow and diffusion models that extends conventional unidimensional coefficients to multidimensional ones and enables inference trajectory-wise adaptation. MAC is trained via simulation-based feedback through adversarial refinement. Empirical results across diverse frameworks and datasets demonstrate that MAC enhances generative quality with high training efficiency. Consequently, our work offers a new perspective on inference trajectory optimality, encouraging future research to move beyond vector field design and to leverage training-efficient, simulation-based optimization. |
| title | Multidimensional Adaptive Coefficient for Inference Trajectory Optimization in Flow and Diffusion |
| topic | Machine Learning Artificial Intelligence I.2.6; I.5.1; F.1.1 |
| url | https://arxiv.org/abs/2404.14161 |