Dataset Pruning: Reducing Training Data by Examining Generalization Influence. (arXiv:2205.09329v1 [cs.LG])

The great success of deep learning heavily relies on increasingly larger
training data, which comes at a price of huge computational and infrastructural
costs. This poses crucial questions that, do all training data contribute to
model's performance? How much does each individual training sample or a
sub-training-set affect the model's generalization, and how to construct a
smallest subset from the entire training data as a proxy training set without
significantly sacrificing the model's performance? To answer these, we propose
dataset …

arxiv data dataset influence pruning training training data