Dynamic Routing Between Capsules

TL;DR

This paper introduces a capsule network architecture that uses dynamic routing to improve recognition of overlapping objects, achieving state-of-the-art results on MNIST by modeling hierarchical relationships more effectively.

Contribution

The paper proposes a novel capsule network with dynamic routing that better captures hierarchical pose relationships and improves recognition accuracy over traditional convolutional networks.

Findings

State-of-the-art performance on MNIST

Superior recognition of overlapping digits

Effective hierarchical pose modeling

Abstract

A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or an object part. We use the length of the activity vector to represent the probability that the entity exists and its orientation to represent the instantiation parameters. Active capsules at one level make predictions, via transformation matrices, for the instantiation parameters of higher-level capsules. When multiple predictions agree, a higher level capsule becomes active. We show that a discrimininatively trained, multi-layer capsule system achieves state-of-the-art performance on MNIST and is considerably better than a convolutional net at recognizing highly overlapping digits. To achieve these results we use an iterative routing-by-agreement mechanism: A lower-level capsule prefers to send its output to higher level capsules whose activity vectors have a big scalar product with the prediction coming from the lower-level capsule.