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2018 | OriginalPaper | Chapter

Affine Transformation Capsule Net

Authors : Runkun Lu, Jianwei Liu, Siming Lian, Xin Zuo

Published in: Trends and Applications in Knowledge Discovery and Data Mining

Publisher: Springer International Publishing

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Abstract

CapsNet is a great attempt to relieve the drawback of CNN, where the routing by agreement method is tolerant to small changes in the viewpoint on one entity inside an image. This ground breaking has attracted the attention of many researchers, however, original CapsNet only utilizes the length of digit capsules in the classification task, which ignores the information of orientation. Based on this, we propose an Affine Transformation Capsule Net (AT-CapsNet) which we leverage both of the length and orientation information of digit capsules by adding a single-layer perceptron substitutes for the operation of computing length of vectors. In addition, we explain AT-CapsNet model’s architecture from five perspectives and further analyse model complexity and the difference between dynamic routing and attention mechanism. The experimental results outperform the efficiency of our proposed algorithm in real world data sets.

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Metadata
Title
Affine Transformation Capsule Net
Authors
Runkun Lu
Jianwei Liu
Siming Lian
Xin Zuo
Copyright Year
2018
Book
Trends and Applications in Knowledge Discovery and Data Mining

Print ISBN: 978-3-030-04502-9

Electronic ISBN: 978-3-030-04503-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-04503-6_24

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