Why Capsule Neural Networks Do Not Scale: Challenging the Dynamic Parse-Tree Assumption

• URL: http://arxiv.org/abs/2301.01583v1
• Date: Wed, 4 Jan 2023 12:59:51 GMT
• Title: Why Capsule Neural Networks Do Not Scale: Challenging the Dynamic Parse-Tree Assumption
• Authors: Matthias Mitterreiter, Marcel Koch, Joachim Giesen, S\"oren Laue
• Abstract summary: Capsule neural networks replace simple, scalar-valued neurons with vector-valued capsules. CapsNet is the first actual implementation of the conceptual idea of capsule neural networks. No work was able to scale the CapsNet architecture to more reasonable-sized datasets.
• Score: 16.223322939363033
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Capsule neural networks replace simple, scalar-valued neurons with vector-valued capsules. They are motivated by the pattern recognition system in the human brain, where complex objects are decomposed into a hierarchy of simpler object parts. Such a hierarchy is referred to as a parse-tree. Conceptually, capsule neural networks have been defined to realize such parse-trees. The capsule neural network (CapsNet), by Sabour, Frosst, and Hinton, is the first actual implementation of the conceptual idea of capsule neural networks. CapsNets achieved state-of-the-art performance on simple image recognition tasks with fewer parameters and greater robustness to affine transformations than comparable approaches. This sparked extensive follow-up research. However, despite major efforts, no work was able to scale the CapsNet architecture to more reasonable-sized datasets. Here, we provide a reason for this failure and argue that it is most likely not possible to scale CapsNets beyond toy examples. In particular, we show that the concept of a parse-tree, the main idea behind capsule neuronal networks, is not present in CapsNets. We also show theoretically and experimentally that CapsNets suffer from a vanishing gradient problem that results in the starvation of many capsules during training.

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