A Trainable Multiplication Layer for Auto-correlation and Co-occurrence Extraction

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

In this paper, we propose a trainable multiplication layer (TML) for a neural network that can be used to calculate the multiplication between the input features. Taking an image as an input, the TML raises each pixel value to the power of a weight and then multiplies them, thereby extracting the higher-order local auto-correlation from the input image. The TML can also be used to extract co-occurrence from the feature map of a convolutional network. The training of the TML is formulated based on backpropagation with constraints to the weights, enabling us to learn discriminative multiplication patterns in an end-to-end manner. In the experiments, the characteristics of the TML are investigated by visualizing learned kernels and the corresponding output features. The applicability of the TML for classification and neural network interpretation is also evaluated using public datasets.

元の言語英語
ホスト出版物のタイトルComputer Vision - ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers
編集者C.V. Jawahar, Hongdong Li, Konrad Schindler, Greg Mori
出版者Springer Verlag
ページ414-430
ページ数17
ISBN(印刷物)9783030208899
DOI
出版物ステータス出版済み - 1 1 2019
イベント14th Asian Conference on Computer Vision, ACCV 2018 - Perth, オーストラリア
継続期間: 12 2 201812 6 2018

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11362 LNCS
ISSN(印刷物)0302-9743
ISSN(電子版)1611-3349

会議

会議14th Asian Conference on Computer Vision, ACCV 2018
オーストラリア
Perth
期間12/2/1812/6/18

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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