Dataset Distillation via Committee Voting
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915798887432192 |
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| author | Cui, Jiacheng Li, Zhaoyi Ma, Xiaochen Bi, Xinyue Luo, Yaxin Shen, Zhiqiang |
| author_facet | Cui, Jiacheng Li, Zhaoyi Ma, Xiaochen Bi, Xinyue Luo, Yaxin Shen, Zhiqiang |
| contents | Dataset distillation aims to synthesize a compact yet representative dataset that preserves the essential characteristics of the original data for efficient model training. Existing methods mainly focus on improving data-synthetic alignment or scaling distillation to large datasets. In this work, we propose $\textbf{C}$ommittee $\textbf{V}$oting for $\textbf{D}$ataset $\textbf{D}$istillation ($\textbf{CV-DD}$), an orthogonal approach that leverages the collective knowledge of multiple models to produce higher-quality distilled data. We first establish a strong baseline that achieves state-of-the-art performance through modern architectural and optimization choices. By integrating distributions and predictions from multiple models and generating high-quality soft labels, our method captures a broader range of data characteristics, reduces model-specific bias and the impact of distribution shifts, and significantly improves generalization. This voting-based strategy enhances diversity and robustness, alleviates overfitting, and improves post-evaluation performance. Extensive experiments across multiple datasets and IPC settings demonstrate that CV-DD consistently outperforms single- and multi-model distillation methods and generalizes well to non-training-based frameworks and challenging synthetic-to-real transfer tasks. Code is available at: https://github.com/Jiacheng8/CV-DD. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07575 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dataset Distillation via Committee Voting Cui, Jiacheng Li, Zhaoyi Ma, Xiaochen Bi, Xinyue Luo, Yaxin Shen, Zhiqiang Computer Vision and Pattern Recognition Artificial Intelligence Dataset distillation aims to synthesize a compact yet representative dataset that preserves the essential characteristics of the original data for efficient model training. Existing methods mainly focus on improving data-synthetic alignment or scaling distillation to large datasets. In this work, we propose $\textbf{C}$ommittee $\textbf{V}$oting for $\textbf{D}$ataset $\textbf{D}$istillation ($\textbf{CV-DD}$), an orthogonal approach that leverages the collective knowledge of multiple models to produce higher-quality distilled data. We first establish a strong baseline that achieves state-of-the-art performance through modern architectural and optimization choices. By integrating distributions and predictions from multiple models and generating high-quality soft labels, our method captures a broader range of data characteristics, reduces model-specific bias and the impact of distribution shifts, and significantly improves generalization. This voting-based strategy enhances diversity and robustness, alleviates overfitting, and improves post-evaluation performance. Extensive experiments across multiple datasets and IPC settings demonstrate that CV-DD consistently outperforms single- and multi-model distillation methods and generalizes well to non-training-based frameworks and challenging synthetic-to-real transfer tasks. Code is available at: https://github.com/Jiacheng8/CV-DD. |
| title | Dataset Distillation via Committee Voting |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2501.07575 |