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 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) |
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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) |
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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 |
6 |
Date of Issue |
2020-12-10 (PRMU) |
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