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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/2503.14559 |
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| _version_ | 1866916656567025664 |
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| author | Lin, Weixiong Ju, Chen Wang, Haicheng Hu, Shengchao Xiao, Shuai Chen, Mengting Jiao, Yuheng Yao, Mingshuai Lan, Jinsong Liu, Qingwen Chen, Ying |
| author_facet | Lin, Weixiong Ju, Chen Wang, Haicheng Hu, Shengchao Xiao, Shuai Chen, Mengting Jiao, Yuheng Yao, Mingshuai Lan, Jinsong Liu, Qingwen Chen, Ying |
| contents | Widely observed data scaling laws, in which error falls off as a power of the training size, demonstrate the diminishing returns of unselective data expansion. Hence, data governance is proposed to downsize datasets through pruning non-informative samples. Yet, isolating the impact of a specific sample on overall model performance is challenging, due to the vast computation required for tryout all sample combinations. Current data governors circumvent this complexity by estimating sample contributions through heuristic-derived scalar scores, thereby discarding low-value ones. Despite thorough sample sieving, retained samples contain substantial undesired tokens intrinsically, underscoring the potential for further compression and purification. In this work, we upgrade data governance from a 'sieving' approach to a 'juicing' one. Instead of scanning for least-flawed samples, our dual-branch DataJuicer applies finer-grained intra-sample governance. It squeezes out informative tokens and boosts image-text alignments. Specifically, the vision branch retains salient image patches and extracts relevant object classes, while the text branch incorporates these classes to enhance captions. Consequently, DataJuicer yields more refined datasets through finer-grained governance. Extensive experiments across datasets demonstrate that DataJuicer significantly outperforms existing DataSieve in image-text retrieval, classification, and dense visual reasoning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14559 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Squeeze Out Tokens from Sample for Finer-Grained Data Governance Lin, Weixiong Ju, Chen Wang, Haicheng Hu, Shengchao Xiao, Shuai Chen, Mengting Jiao, Yuheng Yao, Mingshuai Lan, Jinsong Liu, Qingwen Chen, Ying Machine Learning Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Widely observed data scaling laws, in which error falls off as a power of the training size, demonstrate the diminishing returns of unselective data expansion. Hence, data governance is proposed to downsize datasets through pruning non-informative samples. Yet, isolating the impact of a specific sample on overall model performance is challenging, due to the vast computation required for tryout all sample combinations. Current data governors circumvent this complexity by estimating sample contributions through heuristic-derived scalar scores, thereby discarding low-value ones. Despite thorough sample sieving, retained samples contain substantial undesired tokens intrinsically, underscoring the potential for further compression and purification. In this work, we upgrade data governance from a 'sieving' approach to a 'juicing' one. Instead of scanning for least-flawed samples, our dual-branch DataJuicer applies finer-grained intra-sample governance. It squeezes out informative tokens and boosts image-text alignments. Specifically, the vision branch retains salient image patches and extracts relevant object classes, while the text branch incorporates these classes to enhance captions. Consequently, DataJuicer yields more refined datasets through finer-grained governance. Extensive experiments across datasets demonstrate that DataJuicer significantly outperforms existing DataSieve in image-text retrieval, classification, and dense visual reasoning. |
| title | Squeeze Out Tokens from Sample for Finer-Grained Data Governance |
| topic | Machine Learning Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.14559 |