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Paper Abstract and Keywords
Presentation 2024-03-03 15:00
Learning VQ-VAE for Image Dimensionality Reduction with Spatial Frequency Loss
Naoyuki Ichimura (AIST) PRMU2023-60
Abstract (in Japanese) (See Japanese page) 
(in English) Vector Quantized-Variational AutoEncoders (VQ-VAEs) are a type of deep neural networks designed to learn an approximate identity mapping. Using VQ-VAEs, we can perform both representation learning and dimensionality reduction of a dataset without supervision. This paper proposes a training method for VQ-VAEs with the goal of image dimensionality reduction utilizing the Spatial Frequency Loss (SFL). The SFL is defined as the weighted sum of L2 losses across image subbands. Thus, it can emphasize the losses in high-frequency bands by weighting to reduce the blurs in reconstructed images. Additionally, when using
VQ-VAEs as generators in Generative Adversarial Networks (GANs), subband images used for SFL computation can be input to discriminators, enabling learning with the distribution of subband features. The effectiveness of the proposed method is examined through experiments.
Keyword (in Japanese) (See Japanese page) 
(in English) VQ-VAEs / Dimensionality reduction / Spatial Frequency Loss / Subbands / GANs / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 409, PRMU2023-60, pp. 53-58, March 2024.
Paper # PRMU2023-60 
Date of Issue 2024-02-25 (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 PRMU2023-60

Conference Information
Committee PRMU IBISML IPSJ-CVIM  
Conference Date 2024-03-03 - 2024-03-04 
Place (in Japanese) (See Japanese page) 
Place (in English) Hiroshima Univ. Higashi-Hiroshima campus 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2024-03-PRMU-IBISML-CVIM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Learning VQ-VAE for Image Dimensionality Reduction with Spatial Frequency Loss 
Sub Title (in English)  
Keyword(1) VQ-VAEs  
Keyword(2) Dimensionality reduction  
Keyword(3) Spatial Frequency Loss  
Keyword(4) Subbands  
Keyword(5) GANs  
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1st Author's Name Naoyuki Ichimura  
1st Author's Affiliation National Institute of Advanced Industrial Science and Technology (AIST)
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Speaker Author-1 
Date Time 2024-03-03 15:00:00 
Presentation Time 12 minutes 
Registration for PRMU 
Paper # PRMU2023-60 
Volume (vol) vol.123 
Number (no) no.409 
Page pp.53-58 
#Pages 6 
Date of Issue 2024-02-25 (PRMU) 

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