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| Presentation | 2020-12-17 16:20 [Short Paper] Few-Shot Incremental Learning by Unifying with Variational Autoencoder Keita Takayama, Kuniaki Uto, Koichi Shinoda (TokyoTech)  PRMU2020-48 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | We propose a few-shot incremental learning method using a variational autoencoder for deep learning. In incremental learning, new classes are given in sequence, and the data of the classes previously given are not available for training a classifier. Recently, a method called Generative Replay has achieved high performance by using samples generated by a variational autoencoder (VAE) for training, but its performance degrades when the amount of data of a new class is small. Our proposed method, Few-Shot Generative Replay, solves this problem by simultaneously learning a VAE and a classifier, where the latent variables of VAE are used as the input features for the classifier. By sharing the variance of the latent variable distributions between classes, the resulting model is robust against data insufficiency. We limited number of samples to 10 for each class from the 2nd task of SplitMNIST and evaluated on it. Its accuracy was 64.8%, which was 7.9 points higher than 56.9% of Generative Replay. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Few-Shot Learning / Incremental Learning / Transfer Learning / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 300, PRMU2020-48, pp. 58-62, Dec. 2020. | |
| Paper # | PRMU2020-48 | |
| 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) | |
| Download PDF | PRMU2020-48 |
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| 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 | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Few-Shot Incremental Learning by Unifying with Variational Autoencoder |
| Sub Title (in English) | |
| Keyword(1) | Few-Shot Learning |
| Keyword(2) | Incremental Learning |
| Keyword(3) | Transfer Learning |
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| 1st Author's Name | Keita Takayama |
| 1st Author's Affiliation | Tokyo Institute of Technology (TokyoTech) |
| 2nd Author's Name | Kuniaki Uto |
| 2nd Author's Affiliation | Tokyo Institute of Technology (TokyoTech) |
| 3rd Author's Name | Koichi Shinoda |
| 3rd Author's Affiliation | Tokyo Institute of Technology (TokyoTech) |
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| Speaker | Author-1 |
| Date Time | 2020-12-17 16:20:00 |
| Presentation Time | 10 minutes |
| Registration for | PRMU |
| Paper # | PRMU2020-48 |
| Volume (vol) | vol.120 |
| Number (no) | no.300 |
| Page | pp.58-62 |
| #Pages | 5 |
| Date of Issue | 2020-12-10 (PRMU) |
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