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Paper Abstract and Keywords
Presentation 2021-03-04 10:45
[Short Paper] High-Resolution Image Completion by Hierarchical Neural Process
Masato Miyahara, Daisuke Sato, Masato Fukuda, Narimune Matsumura, Yoshiki Nishikawa (NTT) PRMU2020-74
Abstract (in Japanese) (See Japanese page) 
(in English) Neural Process (NP) is a deep generation model which can consider the uncertainty of prediction.
The unknown output is estimated by learning the stochastic expression of the original function as a latent variable using the known input/output data following the shape unknown function as learning data.
On the other hand, NP assumes Gaussian distribution as a distribution followed by latent variables, so it is difficult to adapt to data following a function represented by a complex distribution with non-Gaussian latent variables.
In this paper, we propose a hierarchical NP which is a hierarchical representation of the latent variables of NPs.
In addition to the hierarchy of NPs, we introduce skip paths and bidirectional latent variables in order to improve the learning efficiency of latent variables in upper layers.
As a result of the comparison experiment using the composite function of the linear function, it was shown that the prediction performance of the proposed method was inferior to the existing method in the simple condition setting.
Keyword (in Japanese) (See Japanese page) 
(in English) Neural Process / Hierarchization / Image Complementation / Meta Learning / Deep Learning / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 409, PRMU2020-74, pp. 31-34, March 2021.
Paper # PRMU2020-74 
Date of Issue 2021-02-25 (PRMU) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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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Conference Information
Committee PRMU IPSJ-CVIM  
Conference Date 2021-03-04 - 2021-03-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Computer Vision and Pattern Recognition for specific environment 
Paper Information
Registration To PRMU 
Conference Code 2021-03-PRMU-CVIM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) High-Resolution Image Completion by Hierarchical Neural Process 
Sub Title (in English)  
Keyword(1) Neural Process  
Keyword(2) Hierarchization  
Keyword(3) Image Complementation  
Keyword(4) Meta Learning  
Keyword(5) Deep Learning  
1st Author's Name Masato Miyahara  
1st Author's Affiliation NTT Service Evolution Laboratories (NTT)
2nd Author's Name Daisuke Sato  
2nd Author's Affiliation NTT Service Evolution Laboratories (NTT)
3rd Author's Name Masato Fukuda  
3rd Author's Affiliation NTT Service Evolution Laboratories (NTT)
4th Author's Name Narimune Matsumura  
4th Author's Affiliation NTT Service Evolution Laboratories (NTT)
5th Author's Name Yoshiki Nishikawa  
5th Author's Affiliation NTT Service Evolution Laboratories (NTT)
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Date Time 2021-03-04 10:45:00 
Presentation Time 10 
Registration for PRMU 
Paper # IEICE-PRMU2020-74 
Volume (vol) IEICE-120 
Number (no) no.409 
Page pp.31-34 
#Pages IEICE-4 
Date of Issue IEICE-PRMU-2021-02-25 

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