L_DMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise.
http://papers.nips.cc/paper/8853-l_dmi-a-novel-information-theoretic-loss-function-for-training-deep-nets-robust-to-label-noise
@inproceedings{DBLP:conf/nips/XuCKW19,
author = {Yilun Xu and
Peng Cao and
Yuqing Kong and
Yizhou Wang},
editor = {Hanna M. Wallach and
Hugo Larochelle and
Alina Beygelzimer and
Florence d'Alch{\'{e}}{-}Buc and
Emily B. Fox and
Roman Garnett},
title = {L{\_}DMI: {A} Novel Information-theoretic Loss Function for Training
Deep Nets Robust to Label Noise},
booktitle = {Advances in Neural Information Processing Systems 32: Annual Conference
on Neural Information Processing Systems 2019, NeurIPS 2019, 8-14
December 2019, Vancouver, BC, Canada},
pages = {6222--6233},
year = {2019},
url = {http://papers.nips.cc/paper/8853-l\_dmi-a-novel-information-theoretic-loss-function-for-training-deep-nets-robust-to-label-noise},
timestamp = {Fri, 06 Mar 2020 16:59:18 +0100},
biburl = {https://dblp.org/rec/conf/nips/XuCKW19.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
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