Tensor train decomposition for efficient memory saving in perceptual feature-maps

Taehyeon Kim, Jieun Lee, Yoonsik Choe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The perceptual loss functions have been used successfully in image transformation for capturing high-level features from images in pre-trained convolutional neural networks (CNNs). Standard perceptual losses require numerous parameters to compare differences in feature-maps on both an input image and a target image; thus, it is not affordable for resource-constrained devices in terms of utilizing a feature-maps. Hence, we propose a compressed perceptual losses oriented Tensor Train (TT) decomposition on the feature-maps. Additionally, to decide an optimal TT-ranks, the proposed algorithm used the global analytic solution of Variational Bayesian Matrix Factorization (VBMF). Therefore, in proposed method, the low-rank approximated feature-maps consist of salient features by virtue of these two techniques. To the best of our knowledge, we are the first to consider curtailing redundancies in feature-maps via low-rank TT-decomposition. Experimental results in style transfer tasks demonstrate that our method not only yields similar qualitative and quantitative results as that of the original version, but also reduces memory requirement by approximately 77%.

Original languageEnglish
Title of host publicationProceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages599-604
Number of pages6
ISBN (Electronic)9781728150239
DOIs
Publication statusPublished - 2019 Oct
Event17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019 - Seoul, Korea, Republic of
Duration: 2019 Oct 272019 Oct 28

Publication series

NameProceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019

Conference

Conference17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019
Country/TerritoryKorea, Republic of
CitySeoul
Period19/10/2719/10/28

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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