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arXiv · 2107.09301

A Bayesian Approach to Invariant Deep Neural Networks

Nikolaos Mourdoukoutas·Marco Federici·Georges Pantalos·Mark van der Wilk·Vincent Fortuin
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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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Nikolaos Mourdoukoutas, Marco Federici, Georges Pantalos, Mark van der Wilk, Vincent Fortuin. 2021-11-02. A Bayesian Approach to Invariant Deep Neural Networks. https://arxiv.org/abs/2107.09301

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