Rotation Invariance in Graph Convolutional Networks

Mac, Nguyen Anh; Nguyen, Hung Son

doi:10.15439/2021F140

Artykuł - szczegóły

Tytuł artykułu

Rotation Invariance in Graph Convolutional Networks

Autorzy

Mac Nguyen Anh , Nguyen Hung Son

Wybrane pełne teksty z tego czasopisma

http://annals-csis.org

Identyfikatory

DOI

10.15439/2021F140

Warianty tytułu

Konferencja

Federated Conference on Computer Science and Information Systems (16 ; 02-05.09.2021 ; online)

Języki publikacji

Abstrakty

Convolution filters in deep convolutional networks display rotation variant behavior. While learned invariant behavior can be partially achieved, this paper shows that current methods of utilizing rotation variant features can be improved by proposing a grid-based graph convolutional network. We demonstrate that Grid-GCN heavily outperforms existing models on rotated images, and through a set of ablation studies, we show how the performance of Grid-GCN implies that there exist more performant methods to utilize fundamentally rotation variant features and we conclude that the inherit nature of spectral graph convolutions is able to learn invariant behavior.

Słowa kluczowe

convolutional neural nets feature extraction graph theory grid computing image processing object recognition

splotowe sieci neuronowe wyodrębnianie funkcji teoria grafów przetwarzanie siatkowe przetwarzanie obrazu rozpoznawanie obiektów

Wydawca

Polskie Towarzystwo Informatyczne

Czasopismo

Annals of Computer Science and Information Systems

Rocznik

2021

Tom

Vol. 25

Strony

81--90

Opis fizyczny

Bibliogr. 41 poz., rys., wz., wykr., tab.

Twórcy

autor

Mac Nguyen Anh

22mac_a@aswarsaw.org

American School of Warsaw Warszawska 202, 05-520 Bielawa, Poland

autor

Nguyen Hung Son

son@mimuw.edu.pl

University of Warsaw Krakowskie Przedmie´scie 26/28, 00-927 Warszawa, Poland

Bibliografia

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Uwagi

1. Track 1: Artificial Intelligence in Applications

2. Session: 15th International Symposium Advances in Artificial Intelligence and Applications

Typ dokumentu

Bibliografia

Identyfikator YADDA

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