Exploring the Equivalence of Closed-Set Generative and Real Data Augmentation in Image Classification
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909735713767424 |
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| author | Wang, Haowen Zhang, Guowei Zhang, Xiang Chen, Zeyuan Xu, Haiyang Kwark, Dou Hoon Tu, Zhuowen |
| author_facet | Wang, Haowen Zhang, Guowei Zhang, Xiang Chen, Zeyuan Xu, Haiyang Kwark, Dou Hoon Tu, Zhuowen |
| contents | In this paper, we address a key scientific problem in machine learning: Given a training set for an image classification task, can we train a generative model on this dataset to enhance the classification performance? (i.e., closed-set generative data augmentation). We start by exploring the distinctions and similarities between real images and closed-set synthetic images generated by advanced generative models. Through extensive experiments, we offer systematic insights into the effective use of closed-set synthetic data for augmentation. Notably, we empirically determine the equivalent scale of synthetic images needed for augmentation. In addition, we also show quantitative equivalence between the real data augmentation and open-set generative augmentation (generative models trained using data beyond the given training set). While it aligns with the common intuition that real images are generally preferred, our empirical formulation also offers a guideline to quantify the increased scale of synthetic data augmentation required to achieve comparable image classification performance. Our results on natural and medical image datasets further illustrate how this effect varies with the baseline training set size and the amount of synthetic data incorporated. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09550 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring the Equivalence of Closed-Set Generative and Real Data Augmentation in Image Classification Wang, Haowen Zhang, Guowei Zhang, Xiang Chen, Zeyuan Xu, Haiyang Kwark, Dou Hoon Tu, Zhuowen Computer Vision and Pattern Recognition In this paper, we address a key scientific problem in machine learning: Given a training set for an image classification task, can we train a generative model on this dataset to enhance the classification performance? (i.e., closed-set generative data augmentation). We start by exploring the distinctions and similarities between real images and closed-set synthetic images generated by advanced generative models. Through extensive experiments, we offer systematic insights into the effective use of closed-set synthetic data for augmentation. Notably, we empirically determine the equivalent scale of synthetic images needed for augmentation. In addition, we also show quantitative equivalence between the real data augmentation and open-set generative augmentation (generative models trained using data beyond the given training set). While it aligns with the common intuition that real images are generally preferred, our empirical formulation also offers a guideline to quantify the increased scale of synthetic data augmentation required to achieve comparable image classification performance. Our results on natural and medical image datasets further illustrate how this effect varies with the baseline training set size and the amount of synthetic data incorporated. |
| title | Exploring the Equivalence of Closed-Set Generative and Real Data Augmentation in Image Classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.09550 |