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| Presentation | 2018-03-18 16:10 Toward image inbetweening using Latent Model Paulino Cristovao (Univ. of Tsukuba), Yusuke Tanimura, Hidemoto Nakada, Hideki Asoh (AIST)  BioX2017-49 PRMU2017-185 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Image interpolation is a well known problem in computer vision. Many approaches are restricted to optical flow and convolutional neural network. In this work, we present an alternative approach based on generative models to generate in between images (interpolation) using variational autoencoders (VAE). The goals are: Gener- ate in between images using hidden structures (latent variables), and yield latent features that generalize well. Our architecture composed of three networks (VAE) that share weights. We train the network feeding three continous frames so that the second latent variables become close to the average of the first and third latent variables. To get interporated image from two images, we can just reconstract the image from the avarage of the two images’ latent variables. We evaluate the result by comparing the ground truth image and the generated one to evaluate the in between image. In addition we show the reconstructed images using the same network. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Variational Autoencoder / Machine Learning / Deep Learning / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 117, no. 514, PRMU2017-185, pp. 79-84, March 2018. | |
| Paper # | PRMU2017-185 | |
| Date of Issue | 2018-03-11 (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 | BioX2017-49 PRMU2017-185 |
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| Conference Information | |
| Committee | PRMU BioX |
| Conference Date | 2018-03-18 - 2018-03-19 |
| 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 | 2018-03-PRMU-BioX |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Toward image inbetweening using Latent Model |
| Sub Title (in English) | |
| Keyword(1) | Variational Autoencoder |
| Keyword(2) | Machine Learning |
| Keyword(3) | Deep Learning |
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| 1st Author's Name | Paulino Cristovao |
| 1st Author's Affiliation | University of Tsukuba (Univ. of Tsukuba) |
| 2nd Author's Name | Yusuke Tanimura |
| 2nd Author's Affiliation | National Institute of Advanced Industrial Science and Technology (AIST) |
| 3rd Author's Name | Hidemoto Nakada |
| 3rd Author's Affiliation | National Institute of Advanced Industrial Science and Technology (AIST) |
| 4th Author's Name | Hideki Asoh |
| 4th Author's Affiliation | National Institute of Advanced Industrial Science and Technology (AIST) |
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| Speaker | Author-1 |
| Date Time | 2018-03-18 16:10:00 |
| Presentation Time | 25 minutes |
| Registration for | PRMU |
| Paper # | BioX2017-49, PRMU2017-185 |
| Volume (vol) | vol.117 |
| Number (no) | no.513(BioX), no.514(PRMU) |
| Page | pp.79-84 |
| #Pages | 6 |
| Date of Issue | 2018-03-11 (BioX, PRMU) |
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