数据修剪效果受冗余、问题样本和样本依赖性影响，噪声下传统方法失效。
Data Pruning: Redundant, Problematic, and Interdependent Samples
- 分析数据冗余、问题样本与样本间依赖对修剪效果的影响
- 在噪声数据中，主流修剪方法性能显著下降
- 适用于关注数据质量的模型训练优化者
深度学习模型的表现不仅取决于数据量，更受数据质量影响。数据修剪通过保留最重要的训练样本，可减少数据集规模并维持相近的测试性能。我们实证研究了两种主流数据修剪方法在有噪和无噪条件下的表现，发现其在显著标签噪声下会失效。研究表明，数据修剪的成功受三个因素显著影响：数据集中的冗余、问题样本的存在以及样本间的相互依赖性。我们在常用基准分类数据集和神经网络架构上进行了详细分析，结果在不同数据分布和训练协议下具有一致性。
原文摘要 · Abstract (English)
The performance of deep learning models is affected by not only data quantity but also data quality. Data pruning is a process by which practitioners can reduce the size of a dataset by only keeping the most important training data points, thereby achieving similar test set performance. We empirically investigate two popular data pruning methods under noisy and noiseless conditions and show that these methods fail in the presence of significant label noise. We highlight that the success of data pruning is distinctly affected by three factors: redundancy in the dataset, the presence of problematic samples, and interdependence between samples. We perform a detailed investigation on commonly used benchmark classification datasets and neural network architectures. We find that our observations are consistent across data distributions and training protocols.
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