Boosting Statistic Learning with Synthetic Data from Pretrained Large Models
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910932670611456 |
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| author | Jiang, Jialong Hu, Wenkang Huang, Jian Jiao, Yuling Liu, Xu |
| author_facet | Jiang, Jialong Hu, Wenkang Huang, Jian Jiao, Yuling Liu, Xu |
| contents | The rapid advancement of generative models, such as Stable Diffusion, raises a key question: how can synthetic data from these models enhance predictive modeling? While they can generate vast amounts of datasets, only a subset meaningfully improves performance. We propose a novel end-to-end framework that generates and systematically filters synthetic data through domain-specific statistical methods, selectively integrating high-quality samples for effective augmentation. Our experiments demonstrate consistent improvements in predictive performance across various settings, highlighting the potential of our framework while underscoring the inherent limitations of generative models for data augmentation. Despite the ability to produce large volumes of synthetic data, the proportion that effectively improves model performance is limited. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_04992 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Boosting Statistic Learning with Synthetic Data from Pretrained Large Models Jiang, Jialong Hu, Wenkang Huang, Jian Jiao, Yuling Liu, Xu Machine Learning Applications The rapid advancement of generative models, such as Stable Diffusion, raises a key question: how can synthetic data from these models enhance predictive modeling? While they can generate vast amounts of datasets, only a subset meaningfully improves performance. We propose a novel end-to-end framework that generates and systematically filters synthetic data through domain-specific statistical methods, selectively integrating high-quality samples for effective augmentation. Our experiments demonstrate consistent improvements in predictive performance across various settings, highlighting the potential of our framework while underscoring the inherent limitations of generative models for data augmentation. Despite the ability to produce large volumes of synthetic data, the proportion that effectively improves model performance is limited. |
| title | Boosting Statistic Learning with Synthetic Data from Pretrained Large Models |
| topic | Machine Learning Applications |
| url | https://arxiv.org/abs/2505.04992 |