基于数据可分性与模型不确定性的高效图像分类数据剪枝方法
Data Pruning via Separability, Integrity, and Model Uncertainty-Aware Importance Sampling
- 融合可分性、完整性与模型不确定性构建新剪枝指标
- 自适应剪枝率，兼顾类内类间分离度，提升剪枝效果
- 适用于多种模型与高剪枝率场景，尤其适合细粒度分类
本文针对图像分类任务中的数据剪枝问题，提出一种基于重要性采样的新型剪枝方法。所提剪枝指标显式考虑数据可分性、数据完整性及模型不确定性；采样过程自适应于剪枝比例，并同时关注类内与类间分离性，进一步提升剪枝有效性。该方法可无缝集成至其他剪枝指标以增强性能。在四个基准数据集（包括细粒度分类场景）上的实验表明，该方法在高剪枝率下仍保持良好扩展性，且在不同分类模型间具有更强泛化能力。
原文摘要 · Abstract (English)
This paper improves upon existing data pruning methods for image classification by introducing a novel pruning metric and pruning procedure based on importance sampling. The proposed pruning metric explicitly accounts for data separability, data integrity, and model uncertainty, while the sampling procedure is adaptive to the pruning ratio and considers both intra-class and inter-class separation to further enhance the effectiveness of pruning. Furthermore, the sampling method can readily be applied to other pruning metrics to improve their performance. Overall, the proposed approach scales well to high pruning ratio and generalizes better across different classification models, as demonstrated by experiments on four benchmark datasets, including the fine-grained classification scenario.
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