We adopt a multi-view approach for analyzing two knowledge transfer settings---learning using privileged information (LUPI) and distillation---in a common framework. Under reasonable assumptions about the complexities of hypothesis spaces, and being optimistic about the expected loss achievable by the student (in distillation) and a transformed teacher predictor (in LUPI), we show that encouraging agreement between the teacher and the student leads to reduced search space. As a result, improved convergence rate can be obtained with regularized empirical risk minimization.
@article{arxiv.1903.03694,
title = {Everything old is new again: A multi-view learning approach to learning using privileged information and distillation},
author = {Weiran Wang},
journal= {arXiv preprint arXiv:1903.03694},
year = {2019}
}