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Coreset selection is powerful in reducing computational costs andaccelerating data processing for deep learning algorithms. It strives toidentify a small subset from large-scale data, so that training only on thesubset practically performs on par with full data. Practitioners regularlydesire to identify the smallest possible coreset in realistic scenes whilemaintaining comparable model performance, to minimize costs and maximizeacceleration. Motivated by this desideratum, for the first time, we pose theproblem of refined coreset selection, in which the minimal coreset size undermodel performance constraints is explored. Moreover, to address this problem,we propose an innovative method, which maintains optimization priority orderover the model performance and coreset size, and efficiently optimizes them inthe coreset selection procedure. Theoretically, we provide the convergenceguarantee of the proposed method. Empirically, extensive experiments confirmits superiority compared with previous strategies, often yielding better modelperformance with smaller coreset sizes.

Refined Coreset Selection: Towards Minimal Coreset Size under Model Performance Constraints