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arXiv · 1810.04041

On Learning and Learned Data Representation by Capsule Networks

Ancheng Lin·Jun Li·Zhenyuan Ma
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Abstract

In this work, we investigate the following: 1) how the routing affects the CapsNet model fitting; 2) how the representation using capsules helps discover global structures in data distribution, and; 3) how the learned data representation adapts and generalizes to new tasks. Our investigation yielded the results some of which have been mentioned in the original paper of CapsNet, they are: 1) the routing operation determines the certainty with which a layer of capsules pass information to the layer above and the appropriate level of certainty is related to the model fitness; 2) in a designed experiment using data with a known 2D structure, capsule representations enable a more meaningful 2D manifold embedding than neurons do in a standard convolutional neural network (CNN), and; 3) compared with neurons of the standard CNN, capsules of successive layers are less coupled and more adaptive to new data distribution.

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Ancheng Lin, Jun Li, Zhenyuan Ma. 2020-06-22. On Learning and Learned Data Representation by Capsule Networks. https://doi.org/10.1109/access.2019.2911622

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