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| Presentation | 2016-11-17 14:00 [Poster Presentation] Analysis of Multimodal Deep Neural Networks -- Towards the elucidation of the modality integration mechanism -- Yoh-ichi Mototake, Takashi Ikegami (unit of Tokyo)  IBISML2016-97 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | With the rapid development of information technology in recent years, several machine learning algorithms that integrate some information which have different modalities such as images, texts, and sounds for generating sentences and any other have been put to practical use. On the other hand, little is known about the mechanisms by which our modalities are integrated. The purpose of this study is to explore the mechanisms through the analysis of the internal state of Multimodal Deep Neural Networks(DNNs) whose learning algorithms are rapidly developed in recent years. First, we assumed that datasets have structures based on the manifold hypothesis, and then computed the manifolds' tangent spaces from the mapping function of DNNs. Finally, we calculated how the geometric structures were converted inside the DNNs. The result of the analysis suggested that Multimodal DNNs have functions to map manifold structures of the distribution of datasets to a global coordinate system, and that is processed after the integration of the modalities. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Multimodal Deep Learning / Manifold Hypothesis / / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 116, no. 300, IBISML2016-97, pp. 369-373, Nov. 2016. | |
| Paper # | IBISML2016-97 | |
| Date of Issue | 2016-11-09 (IBISML) | |
| 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 | IBISML2016-97 |
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| Conference Information | |
| Committee | IBISML |
| Conference Date | 2016-11-16 - 2016-11-18 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Kyoto Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Information-Based Induction Science Workshop (IBIS2016) |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2016-11-IBISML |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Analysis of Multimodal Deep Neural Networks |
| Sub Title (in English) | Towards the elucidation of the modality integration mechanism |
| Keyword(1) | Multimodal Deep Learning |
| Keyword(2) | Manifold Hypothesis |
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| 1st Author's Name | Yoh-ichi Mototake |
| 1st Author's Affiliation | University of Tokyo (unit of Tokyo) |
| 2nd Author's Name | Takashi Ikegami |
| 2nd Author's Affiliation | University of Tokyo (unit of Tokyo) |
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| Speaker | Author-1 |
| Date Time | 2016-11-17 14:00:00 |
| Presentation Time | 180 minutes |
| Registration for | IBISML |
| Paper # | IBISML2016-97 |
| Volume (vol) | vol.116 |
| Number (no) | no.300 |
| Page | pp.369-373 |
| #Pages | 5 |
| Date of Issue | 2016-11-09 (IBISML) |
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