<ul><li>Data pruning is crucial for selecting a representative subset from a large dataset for deep learning models.</li><li>Geometric Median (GM) Matching is a novel k-subset selection strategy that leverages robust estimation to enhance resilience against noisy data.</li><li>GM Matching enjoys improved convergence rate and outperforms existing pruning approaches in high-corruption settings and high pruning rates.</li><li>It is considered a strong baseline for robust data pruning according to extensive experiments.</li></ul>

Geometric Median Matching for Robust k-Subset Selection from Noisy Data

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