Show HN: Beating Hinton et al.'s capsule net with fewer params and less training https://ift.tt/2peqh4t

Show HN: 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://ift.tt/36cfoki 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 traces of internal code and release this as stand-alone open-source software without having to disclose any key IP. I've reached out to academics in different groups for feedback, and the response so far has been positive, although most have only skimmed the paper. It will likely take a few weeks to get proper feedback from academia. In the meantime, I figured there are a lot of super-smart, knowledgeable people on HN who would love to take a look at this and share their thoughts. Please feel free to ask questions. Let me know what you think! [0] https://ift.tt/36kuG6i October 30, 2019 at 03:29AM