Beating Hinton et al.'s capsule net with fewer params and less training
Hello HN, I recently posted a work-in-progress paper, along with code necessary for replicating all its results, at: https://github.com/glassroom/heinsen_routing Among other things, the code in this repo outperforms Hinton et al.'s recent state-of-the-art result in visual recognition[0] while requiring fewer parameters and an order-of-magnitude fewer training epochs. Most of the original research we do at work tends to be either proprietary in nature or tightly coupled to internal code, so we cannot share it with the world. In this case, however, I was able to remove all…
Beating Hinton et al.'s capsule net with fewer params and less training launched on October 30, 2019 with 14 votes, #883 of 3,363 launches that month and more than 75% of that year's launches.
In plain words
This is an open-source implementation of a capsule network architecture for visual recognition that achieves better performance than Hinton et al.'s recent benchmark while using fewer parameters and significantly less training time. The code and accompanying research paper are available for researchers and developers working on neural network architectures. The project demonstrates that more efficient approaches to capsule networks are possible without requiring extensive computational resources.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hello HN, I recently posted a work-in-progress paper, along with code necessary for replicating all its results, at: https://github.com/glassroom/heinsen_routing Among other things, the code in this repo outperforms Hinton et al.'s recent state-of-the-art result in visual recognition[0] while…
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