Data subset selection aims to find a smaller yet informative subset of a large dataset that can approximate the full-dataset training, addressing challenges associated with training neural networks on large-scale datasets. However, existing methods tend to specialize in either high or low selection ratio regimes, lacking a universal approach that consistently achieves competitive performance across a broad range of selection ratios. We introduce a universal and efficient data subset selection method, Best Window Selection (BWS), by proposing a method to choose the best window subset from samples ordered based on their difficulty scores. This approach offers flexibility by allowing the choice of window intervals that span from easy to difficult samples. Furthermore, we provide an efficient mechanism for selecting the best window subset by evaluating its quality using kernel ridge regression. Our experimental results demonstrate the superior performance of BWS compared to other baselines across a broad range of selection ratios over datasets, including CIFAR-10/100 and ImageNet, and the scenarios involving training from random initialization or fine-tuning of pre-trained models.

提出一种通用且高效的数据子集选择方法Best Window Selection (BWS)，通过根据样本的困难程度对其进行排序，并选择最佳的窗口子集，通过使用核脊回归评估其质量来提供有效的机制，实验证明其在各种选择比例以及使用CIFAR-10/100和ImageNet数据集进行训练的情况下，相较于其他基准线具有更优越的性能。

基于样本得分的最佳窗口选择方法用于广域数据修剪