NeurIPS 2019poster266 citations
L_DMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise
Yilun Xu, Peng Cao, Yuqing Kong, Yizhou Wang
Abstract
Accurately annotating large scale dataset is notoriously expensive both in time and in money. Although acquiring low-quality-annotated dataset can be much cheaper, it often badly damages the performance of trained models when using such dataset without particular treatment. Various methods have been proposed for learning with noisy labels. However, most methods only handle limited kinds of noise patterns, require auxiliary information or steps (e.g., knowing or estimating the noise transition matrix), or lack theoretical justification. In this paper, we propose a novel information-theoretic loss function, L
BibTeX
@inproceedings{NEURIPS2019_8a1ee9f2,
author = {Xu, Yilun and Cao, Peng and Kong, Yuqing and Wang, Yizhou},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {L\_DMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/8a1ee9f2b7abe6e88d1a479ab6a42c5e-Paper.pdf},
volume = {32},
year = {2019}
}