Introducing Routing Uncertainty in Capsule Networks

de Sousa Ribeiro, Fabio, Leontidis, George and Kollias, Stefanos (2020) Introducing Routing Uncertainty in Capsule Networks. In: 2020 Conference on Neural Information Processing Systems, December 6-12, 2020, Vancouver, Canada.

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Abstract

Rather than performing inefficient local iterative routing between adjacent capsule
layers, we propose an alternative global view based on representing the inherent uncertainty
in part-object assignment. In our formulation, the local routing iterations
are replaced with variational inference of part-object connections in a probabilistic
capsule network, leading to a significant speedup without sacrificing performance.
In this way, global context is also considered when routing capsules by introducing
global latent variables that have direct influence on the objective function, and
are updated discriminatively in accordance with the minimum description length
(MDL) principle. We focus on enhancing capsule network properties, and perform a
thorough evaluation on pose-aware tasks, observing improvements in performance
over previous approaches whilst being more computationally efficient.

Keywords:capsule neural networs, routing uncertainty, variational inference, global latent variables
Subjects:G Mathematical and Computer Sciences > G500 Information Systems
G Mathematical and Computer Sciences > G400 Computer Science
G Mathematical and Computer Sciences > G760 Machine Learning
G Mathematical and Computer Sciences > G730 Neural Computing
Divisions:College of Science > School of Computer Science
ID Code:43047
Deposited On:18 Nov 2020 11:48

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