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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.06614 |
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| _version_ | 1866910043995111424 |
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| author | Li, Zhengguo Zheng, Chaobing Wang, Wei |
| author_facet | Li, Zhengguo Zheng, Chaobing Wang, Wei |
| contents | Based on literature review about existing diffusion models and flow matching with a neural network to predict a predefined target from noisy data, a unified representation is first proposed for these models using two simple linear equations in this paper. Theoretical analysis of the proposed model is then presented. Our theoretical analysis shows that the correlation between the noisy data and the predicted target is sometimes weak in the existing diffusion models and flow matching. This might affect the prediction (or learning) process which plays a crucial role in all models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_06614 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Correlation Analysis of Generative Models Li, Zhengguo Zheng, Chaobing Wang, Wei Machine Learning Computer Vision and Pattern Recognition Based on literature review about existing diffusion models and flow matching with a neural network to predict a predefined target from noisy data, a unified representation is first proposed for these models using two simple linear equations in this paper. Theoretical analysis of the proposed model is then presented. Our theoretical analysis shows that the correlation between the noisy data and the predicted target is sometimes weak in the existing diffusion models and flow matching. This might affect the prediction (or learning) process which plays a crucial role in all models. |
| title | Correlation Analysis of Generative Models |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.06614 |