Paper Abstract and Keywords |
Presentation |
2021-03-02 10:00
Learning from Noisy Complementary Labels with Robust Loss Functions Hiroki Ishiguro (UTokyo), Takashi Ishida (UTokyo/RIKEN), Masashi Sugiyama (RIKEN/UTokyo) IBISML2020-34 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
It has been demonstrated that large-scale labeled datasets facilitate the success of machine learning. However, collecting labeled data is often very costly and error-prone in practice. To cope with this problem, previous studies have considered the use of a complementary label, which specifies a class that an instance does not belong to and can be collected more easily than ordinary labels. However, complementary labels could also be error-prone and thus mitigating the influence of label noise is an important challenge to make complementary-label learning more useful in practice. In this paper, we derive conditions for the loss function such that the learning algorithm is not affected by noise in complementary labels. Experiments on benchmark datasets with noisy complementary labels demonstrate that the loss functions that satisfy our conditions significantly improve the classification performance. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
complementary label / label noise / robust loss function / loss correction / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 120, no. 395, IBISML2020-34, pp. 1-8, March 2021. |
Paper # |
IBISML2020-34 |
Date of Issue |
2021-02-23 (IBISML) |
ISSN |
Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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IBISML2020-34 |
Conference Information |
Committee |
IBISML |
Conference Date |
2021-03-02 - 2021-03-04 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Organized and general sessions on machine learning |
Paper Information |
Registration To |
IBISML |
Conference Code |
2021-03-IBISML |
Language |
English (Japanese title is available) |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Learning from Noisy Complementary Labels with Robust Loss Functions |
Sub Title (in English) |
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complementary label |
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label noise |
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robust loss function |
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loss correction |
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1st Author's Name |
Hiroki Ishiguro |
1st Author's Affiliation |
The University of Tokyo (UTokyo) |
2nd Author's Name |
Takashi Ishida |
2nd Author's Affiliation |
The University of Tokyo/RIKEN (UTokyo/RIKEN) |
3rd Author's Name |
Masashi Sugiyama |
3rd Author's Affiliation |
RIKEN/The University of Tokyo (RIKEN/UTokyo) |
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Speaker |
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Date Time |
2021-03-02 10:00:00 |
Presentation Time |
25 minutes |
Registration for |
IBISML |
Paper # |
IBISML2020-34 |
Volume (vol) |
vol.120 |
Number (no) |
no.395 |
Page |
pp.1-8 |
#Pages |
8 |
Date of Issue |
2021-02-23 (IBISML) |
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