{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dynamic-routing-between-capsules","title":"Dynamic Routing Between Capsules","arxiv_id":"1710.09829","date":"2017-10-26","proceeding":"NeurIPS 2017 12","authors":["Sara Sabour","Nicholas Frosst","Geoffrey E. Hinton"],"abstract":"A capsule is a group of neurons whose activity vector represents the\ninstantiation parameters of a specific type of entity such as an object or an\nobject part. We use the length of the activity vector to represent the\nprobability that the entity exists and its orientation to represent the\ninstantiation parameters. Active capsules at one level make predictions, via\ntransformation matrices, for the instantiation parameters of higher-level\ncapsules. When multiple predictions agree, a higher level capsule becomes\nactive. We show that a discrimininatively trained, multi-layer capsule system\nachieves state-of-the-art performance on MNIST and is considerably better than\na convolutional net at recognizing highly overlapping digits. To achieve these\nresults we use an iterative routing-by-agreement mechanism: A lower-level\ncapsule prefers to send its output to higher level capsules whose activity\nvectors have a big scalar product with the prediction coming from the\nlower-level capsule.","url_abs":"http://arxiv.org/abs/1710.09829v2","url_pdf":"http://arxiv.org/pdf/1710.09829v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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