Invariant integration in deep convolutional feature space

Matthias Rath, Alexandru P. Condurache

Abstract

In this contribution, we show how to incorporate prior knowledge to a deep neural network architecture in a principled manner. We enforce feature space invariances using a novel layer based on invariant integration. This allows us to construct a complete feature space invariant to finite transformation groups. We apply our proposed layer to explicitly insert invariance properties for vision-related classification tasks, demonstrate our approach for the case of rotation invariance and report state-of-the-art performance on the Rotated-MNIST dataset. Our method is especially beneficial when training with limited data.

Original languageEnglish
Pages103-108
Number of pages6
Publication statusPublished - 10.2020
EventEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning 2020 - Brügge, Belgium
Duration: 02.10.202004.10.2020

Conference

ConferenceEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning 2020
Abbreviated titleESANN 2020
Country/TerritoryBelgium
CityBrügge
Period02.10.2004.10.20

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