Unsupervised Selective Labeling for More Effective Semi-Supervised Learning. (arXiv:2110.03006v3 [cs.LG] UPDATED)

Given an unlabeled dataset and an annotation budget, we study how to
selectively label a fixed number of instances so that semi-supervised learning
(SSL) on such a partially labeled dataset is most effective. We focus on
selecting the right data to label, in addition to usual SSL's propagating
labels from labeled data to the rest unlabeled data. This instance selection
task is challenging, as without any labeled data we do not know what the
objective of learning should be. Intuitively, …

arxiv labeling semi-supervised semi-supervised learning supervised learning unsupervised