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| Presentation | 2019-03-18 10:00 A Generative Self-Ensemble Approach to Simulated+Unsupervised Learning Yu Mitsuzumi (Kyoto Univ.), Go Irie (NTT), Atsushi Nakazawa (Kyoto Univ.), Akisato Kimura (NTT)  BioX2018-52 PRMU2018-156 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | The simulated and unsupervised (S+U) learning framework is an effective approach in computer vision since it solves various recognition tasks without using labeled real images. Although both labeled synthetic and unlabeled real images are available, existing S+U learning methods use only the labeled synthetic images for training predictors (regression functions or classifiers), which may prevent from leveraging information of the target domain. In this paper, we propose a novel S+U learning approach that utilizes both synthetic and real images to improve the prediction performance in the real domain. Our method consists of a) unsupervised learning of one-to-many translations that can generate a wide variety of ``fake'' images from a single real image with preserving their labels, and b) semi-supervised self-ensemble learning that gains increased prediction accuracy by using both labeled synthetic and unlabeled real images. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Deep Learning / Generative Adversarial Nets / Semi-supervised Learning / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 513, PRMU2018-156, pp. 137-142, March 2019. | |
| Paper # | PRMU2018-156 | |
| Date of Issue | 2019-03-10 (BioX, PRMU) | |
| ISSN | Print edition: ISSN 0913-5685 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 | BioX2018-52 PRMU2018-156 |
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| Conference Information | |
| Committee | PRMU BioX |
| Conference Date | 2019-03-17 - 2019-03-18 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | PRMU |
| Conference Code | 2019-03-PRMU-BioX |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Generative Self-Ensemble Approach to Simulated+Unsupervised Learning |
| Sub Title (in English) | |
| Keyword(1) | Deep Learning |
| Keyword(2) | Generative Adversarial Nets |
| Keyword(3) | Semi-supervised Learning |
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| 1st Author's Name | Yu Mitsuzumi |
| 1st Author's Affiliation | Kyoto University (Kyoto Univ.) |
| 2nd Author's Name | Go Irie |
| 2nd Author's Affiliation | Nippon Telegraph and Telephone Corporation (NTT) |
| 3rd Author's Name | Atsushi Nakazawa |
| 3rd Author's Affiliation | Kyoto University (Kyoto Univ.) |
| 4th Author's Name | Akisato Kimura |
| 4th Author's Affiliation | Nippon Telegraph and Telephone Corporation (NTT) |
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| Speaker | Author-1 |
| Date Time | 2019-03-18 10:00:00 |
| Presentation Time | 15 minutes |
| Registration for | PRMU |
| Paper # | BioX2018-52, PRMU2018-156 |
| Volume (vol) | vol.118 |
| Number (no) | no.512(BioX), no.513(PRMU) |
| Page | pp.137-142 |
| #Pages | 6 |
| Date of Issue | 2019-03-10 (BioX, PRMU) |
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