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They are not hyped. Capsule nets are in early phase of research.

The bleeding edge deep learning research that tries to push the science forward is trying to develop new ideas. Hinton et. al developed the current deep learning revolution gradually over decades. Next non-incremental revolutions in the field may take similar time to mature.

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Oh for sure! Not everybody needs to be trying to max out the standard benchmarks with every paper. But have there been any new developments or improvements with capsule nets since the original paper? This blog post looks like it only references that first one, which is coming up on two years old, right? It'd be great to read about any followup.

Graph Capsule Convolutional Networks [0] claim to have beaten the state of the art in graph neural networks. However, I can’t find information on training time, which I think is significantly more expensive for capsule networks.

[0] https://arxiv.org/abs/1805.08090

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