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arXiv · 2004.09166

Invariant Integration in Deep Convolutional Feature Space

Matthias Rath·Alexandru Paul Condurache
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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.

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Matthias Rath, Alexandru Paul Condurache. 2020-04-20. Invariant Integration in Deep Convolutional Feature Space. https://arxiv.org/abs/2004.09166

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