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

arXiv:2311.08675v2 Announce Type: replace
Abstract: 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. Practitioners regularly desire to identify the smallest possible coreset in realistic scenes while maintaining comparable model performance, to minimize costs and maximize acceleration. Motivated by this desideratum, for the first time, we pose the problem …

abstract algorithms arxiv computational constraints costs cs.lg data data processing deep learning deep learning algorithms identify performance processing scale small training type