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

Hideaki Hayashi, Seiichi Uchida

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

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationComputer Vision - ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers
EditorsC.V. Jawahar, Hongdong Li, Konrad Schindler, Greg Mori
PublisherSpringer Verlag
Number of pages17
ISBN (Print)9783030208899
Publication statusPublished - 2019
Event14th Asian Conference on Computer Vision, ACCV 2018 - Perth, Australia
Duration: Dec 2 2018Dec 6 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11362 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th Asian Conference on Computer Vision, ACCV 2018

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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