Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy. (arXiv:2311.01002v1 [cs.LG])

Data pruning, which aims to downsize a large training set into a small
informative subset, is crucial for reducing the enormous computational costs of
modern deep learning. Though large-scale data collections invariably contain
annotation noise and numerous robust learning methods have been developed, data
pruning for the noise-robust learning scenario has received little attention.
With state-of-the-art Re-labeling methods that self-correct erroneous labels
while training, it is challenging to identify which subset induces the most
accurate re-labeling of erroneous labels in …

accuracy annotation arxiv computational costs data data pruning deep learning labeling modern noise pruning scale set small training