A Bayesian Approach to Invariant Deep Neural Networks

no code implementations • 20 Jul 2021 • Nikolaos Mourdoukoutas, Marco Federici, Georges Pantalos, Mark van der Wilk, Vincent Fortuin

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.

Data Augmentation