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Paper Abstract and Keywords
Presentation 2021-10-09 09:15
Cross-modal CycleGAN for Low-Resource Anime Style Face Translation
Shiping Deng (Hosei Univ./USTC), Kaoru Uchida (Hosei Univ.) PRMU2021-18
Abstract (in Japanese) (See Japanese page) 
(in English) Human face to anime face translation has attracted the attention of many researchers in recent
years, and various works have achieved high-quality style transfer on conventional tasks. However,
existing works often have fatal shortcomings when the target domain training data is heavily
insufficient, which is named as imbalanced (low-resource) setting. Here the low-resource task,
generally means there is no sufficient data on the training dataset compared with the conventional
task, e.g. the training data size is fewer than 100. To solve this problem, we propose a multimodal
low-resource translation model for a specific style. Based on the cyclic adversarial network
and class activation map, we import semantic modality to enhance data information and attention
modules, which will help our model focus more on the important areas distinguishing the source
domain from the target domain. Unlike the previous unsupervised learning of single modality, our
model successfully completes image translation in cross-modal situations by importing pre-trained
text-image alignment model. In addition, the use of an asymmetric structure speeds up training and
flexibly generates images of the target domain. The experimental results show that our method has
superiority in low-resource settings compared with the existing work of the same type.
Keyword (in Japanese) (See Japanese page) 
(in English) Data-imbalanced / style translation / deep neural network / / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 192, PRMU2021-18, pp. 11-16, Oct. 2021.
Paper # PRMU2021-18 
Date of Issue 2021-10-01 (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)
Download PDF PRMU2021-18

Conference Information
Committee PRMU  
Conference Date 2021-10-08 - 2021-10-09 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Processes and technologies to make research more efficient 
Paper Information
Registration To PRMU 
Conference Code 2021-10-PRMU 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Cross-modal CycleGAN for Low-Resource Anime Style Face Translation 
Sub Title (in English)  
Keyword(1) Data-imbalanced  
Keyword(2) style translation  
Keyword(3) deep neural network  
1st Author's Name Shiping Deng  
1st Author's Affiliation Hosei University/University of Science and Technology of China (Hosei Univ./USTC)
2nd Author's Name Kaoru Uchida  
2nd Author's Affiliation Hosei University (Hosei Univ.)
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Speaker Author-1 
Date Time 2021-10-09 09:15:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2021-18 
Volume (vol) vol.121 
Number (no) no.192 
Page pp.11-16 
Date of Issue 2021-10-01 (PRMU) 

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