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Data pruning, which aims to downsize a large training set into a smallinformative subset, is crucial for reducing the enormous computational costs ofmodern deep learning. Though large-scale data collections invariably containannotation noise and numerous robust learning methods have been developed, datapruning for the noise-robust learning scenario has received little attention.With state-of-the-art Re-labeling methods that self-correct erroneous labelswhile training, it is challenging to identify which subset induces the mostaccurate re-labeling of erroneous labels in the entire training set. In thispaper, we formalize the problem of data pruning with re-labeling. We first showthat the likelihood of a training example being correctly re-labeled isproportional to the prediction confidence of its neighborhood in the subset.Therefore, we propose a novel data pruning algorithm, Prune4Rel, that finds asubset maximizing the total neighborhood confidence of all training examples,thereby maximizing the re-labeling accuracy and generalization performance.Extensive experiments on four real and one synthetic noisy datasets show that\algname{} outperforms the baselines with Re-labeling models by up to 9.1% aswell as those with a standard model by up to 21.6%.

Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy