Coreset selection is powerful in reducing computational costs and
accelerating data processing for deep learning algorithms. It strives to
identify a small subset from large-scale data, so that training only on the
subset practically performs on par with full data. When coreset selection is
applied in realistic scenes, under the premise that the identified coreset has
achieved comparable model performance, practitioners regularly desire the
identified coreset can have a size as small as possible for lower costs and
greater acceleration. Motivated by this desideratum, for the first time, we
pose the problem of "coreset selection with prioritized multiple objectives",
in which the smallest coreset size under model performance constraints is
explored. Moreover, to address this problem, an innovative method is proposed,
which maintains optimization priority order over the model performance and
coreset size, and efficiently optimizes them in the coreset selection
procedure. Theoretically, we provide the convergence guarantee of the proposed
method. Empirically, extensive experiments confirm its superiority compared
with previous strategies, often yielding better model performance with smaller
coreset sizes.

深度学习算法中，通过核心集选择来减少计算成本和加速数据处理。在满足模型性能的前提下，通过优先级顺序，我们提出了一种创新方法来选择尽可能小的核心集，以更低的成本和更高的加速度实现更好的模型性能。