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| Paper Abstract and Keywords | ||
| Presentation | 2022-03-29 14:15 Weighted Dice Loss for Segmentation from Noisy Labels Toshikazu Samura, Katsumi Tadamura (Yamaguchi Univ.)  MSS2021-77 NLP2021-148 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Deep neural networks (DNNs) are easily affected by noisy labels during training. It degrades the classification performance of DNNs. In this study, we proposed weighted Dice loss for decreasing the adverse effects of noisy labels in a segmentation task. The cleanness of label is reflected to the learning progress. The weighted Dice loss reduces the weights for presumed noisy pixels according to the current epoch and their learning progress. We applied the U-Net trained through the proposed loss into road extraction from aerial images. Consequently, we demonstrated that the weighted Dice loss decreases the adverse effects of noisy labels in the segmentation task. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Weighted Dice loss / Noisy label / U-Net / Segmentation / Road extraction / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 444, NLP2021-148, pp. 117-120, March 2022. | |
| Paper # | NLP2021-148 | |
| Date of Issue | 2022-03-21 (MSS, NLP) | |
| 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) | |
| Download PDF | MSS2021-77 NLP2021-148 |
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| Conference Information | |
| Committee | MSS NLP |
| Conference Date | 2022-03-28 - 2022-03-29 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | MSS, NLP, Work In Progress (MSS only), and etc. |
| Paper Information | |
| Registration To | NLP |
| Conference Code | 2022-03-MSS-NLP |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Weighted Dice Loss for Segmentation from Noisy Labels |
| Sub Title (in English) | |
| Keyword(1) | Weighted Dice loss |
| Keyword(2) | Noisy label |
| Keyword(3) | U-Net |
| Keyword(4) | Segmentation |
| Keyword(5) | Road extraction |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Toshikazu Samura |
| 1st Author's Affiliation | Yamaguchi University (Yamaguchi Univ.) |
| 2nd Author's Name | Katsumi Tadamura |
| 2nd Author's Affiliation | Yamaguchi University (Yamaguchi Univ.) |
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| Speaker | Author-1 |
| Date Time | 2022-03-29 14:15:00 |
| Presentation Time | 25 minutes |
| Registration for | NLP |
| Paper # | MSS2021-77, NLP2021-148 |
| Volume (vol) | vol.121 |
| Number (no) | no.443(MSS), no.444(NLP) |
| Page | pp.117-120 |
| #Pages | 4 |
| Date of Issue | 2022-03-21 (MSS, NLP) |
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