A Bayesian Approach to Invariant Deep Neural Networks

• URL: http://arxiv.org/abs/2107.09301v1
• Date: Tue, 20 Jul 2021 07:33:58 GMT
• Title: A Bayesian Approach to Invariant Deep Neural Networks
• Authors: Nikolaos Mourdoukoutas, Marco Federici, Georges Pantalos, Mark van der Wilk and Vincent Fortuin
• Abstract summary: We show that our model outperforms other non-invariant architectures, when trained on datasets that contain specific invariances. The same holds true when no data augmentation is performed.
• Score: 14.807284992678762
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel Bayesian neural network architecture that can learn invariances from data alone by inferring a posterior distribution over different weight-sharing schemes. We show that our model outperforms other non-invariant architectures, when trained on datasets that contain specific invariances. The same holds true when no data augmentation is performed.

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