Regularized Pooling

Takato Otsuzuki, Hideaki Hayashi, Yuchen Zheng, Seiichi Uchida

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

Abstract

In convolutional neural networks (CNNs), pooling operations play important roles such as dimensionality reduction and deformation compensation. In general, max pooling, which is the most widely used operation for local pooling, is performed independently for each kernel. However, the deformation may be spatially smooth over the neighboring kernels. This means that max pooling is too flexible to compensate for actual deformations. In other words, its excessive flexibility risks canceling the essential spatial differences between classes. In this paper, we propose regularized pooling, which enables the value selection direction in the pooling operation to be spatially smooth across adjacent kernels so as to compensate only for actual deformations. The results of experiments on handwritten character images and texture images showed that regularized pooling not only improves recognition accuracy but also accelerates the convergence of learning compared with conventional pooling operations.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2020 - 29th International Conference on Artificial Neural Networks, Proceedings
EditorsIgor Farkaš, Paolo Masulli, Stefan Wermter
PublisherSpringer Science and Business Media Deutschland GmbH
Pages241-254
Number of pages14
ISBN (Print)9783030616151
DOIs
Publication statusPublished - 2020
Event29th International Conference on Artificial Neural Networks, ICANN 2020 - Bratislava, Slovakia
Duration: Sep 15 2020Sep 18 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12397 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference29th International Conference on Artificial Neural Networks, ICANN 2020
CountrySlovakia
CityBratislava
Period9/15/209/18/20

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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