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Paper Abstract and Keywords
Presentation 2020-12-17 16:30
Towards Discovery of Relevant Latent Factors with Limited Data
Mohit Chhabra, Quan Kong, Tomoaki Yoshinaga (Hitachi) PRMU2020-49
Abstract (in Japanese) (See Japanese page) 
(in English) The remarkable effectiveness of neural networks on vision tasks has led to an interest in adapting neural network models to limited data cases. It is also desired that low dimensional representations of the data efficiently represent the data distribution. We propose to minimize ordinal energy of the code produced by encoder model of de-noising auto-encoder and add stochastic non-linear units. Proposed modifications lead to an increase in the classification performance in the semi-supervised
setting on MNIST, improved lung segmentation results, failure prediction capability on chest scans of COVID19 patients and improved anomaly detection scores on MIMII dataset.
Keyword (in Japanese) (See Japanese page) 
(in English) Representation learning / Anomaly detection / Small Data / Stochastic nonlinearity / De-noising auto-encoder / Segmentation / Ordinal Energy /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 300, PRMU2020-49, pp. 63-68, Dec. 2020.
Paper # PRMU2020-49 
Date of Issue 2020-12-10 (PRMU) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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 PRMU2020-49

Conference Information
Committee PRMU  
Conference Date 2020-12-17 - 2020-12-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Transfer learning and few shot learning 
Paper Information
Registration To PRMU 
Conference Code 2020-12-PRMU 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Towards Discovery of Relevant Latent Factors with Limited Data 
Sub Title (in English)  
Keyword(1) Representation learning  
Keyword(2) Anomaly detection  
Keyword(3) Small Data  
Keyword(4) Stochastic nonlinearity  
Keyword(5) De-noising auto-encoder  
Keyword(6) Segmentation  
Keyword(7) Ordinal Energy  
Keyword(8)  
1st Author's Name Mohit Chhabra  
1st Author's Affiliation Lumada Data Science Lab. Hitachi, Ltd. (Hitachi)
2nd Author's Name Quan Kong  
2nd Author's Affiliation Lumada Data Science Lab. Hitachi, Ltd. (Hitachi)
3rd Author's Name Tomoaki Yoshinaga  
3rd Author's Affiliation Lumada Data Science Lab. Hitachi, Ltd. (Hitachi)
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Speaker Author-1 
Date Time 2020-12-17 16:30:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2020-49 
Volume (vol) vol.120 
Number (no) no.300 
Page pp.63-68 
#Pages
Date of Issue 2020-12-10 (PRMU) 


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