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Rotation-Invariant Convolution Networks with Hexagon-Based Kernels

Yiping TANG, Kohei HATANO, Eiji TAKIMOTO

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Summary :

We introduce the Hexagonal Convolutional Neural Network (HCNN), a modified version of CNN that is robust against rotation. HCNN utilizes a hexagonal kernel and a multi-block structure that enjoys more degrees of rotation information sharing than standard convolution layers. Our structure is easy to use and does not affect the original tissue structure of the network. We achieve the complete rotational invariance on the recognition task of simple pattern images and demonstrate better performance on the recognition task of the rotated MNIST images, synthetic biomarker images and microscopic cell images than past methods, where the robustness to rotation matters.

Publication
IEICE TRANSACTIONS on Information Vol.E107-D No.2 pp.220-228
Publication Date
2024/02/01
Publicized
2023/11/15
Online ISSN
1745-1361
DOI
10.1587/transinf.2023EDP7023
Type of Manuscript
PAPER
Category
Biocybernetics, Neurocomputing

Authors

Yiping TANG
  Kyushu University
Kohei HATANO
  Kyushu University,Riken AIP
Eiji TAKIMOTO
  Kyushu University

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