Invariant Integration in Deep Convolutional Feature Space

• URL: http://arxiv.org/abs/2004.09166v1
• Date: Mon, 20 Apr 2020 09:45:43 GMT
• Title: Invariant Integration in Deep Convolutional Feature Space
• Authors: Matthias Rath and Alexandru Paul Condurache
• Abstract summary: We show how to incorporate prior knowledge to a deep neural network architecture in a principled manner. We report state-of-the-art performance on the Rotated-MNIST dataset.
• Score: 77.99182201815763
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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