Transcription of Dynamic Routing Between Capsules
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Dynamic Routing Between Capsules
Dynamic Routing Between CapsulesSara SabourNicholas FrosstGeoffrey E. HintonGoogle BrainToronto{sasabour, frosst, capsule is a group of neurons whose activity vector represents the instantiationparameters of a specific type of entity such as an object or an object part. We usethe length of the activity vector to represent the probability that the entity exists andits orientation to represent the instantiation parameters. Active Capsules at one levelmake predictions, via transformation matrices, for the instantiation parameters ofhigher-level Capsules . When multiple predictions agree, a higher level capsulebecomes active. We show that a discrimininatively trained, multi-layer capsulesystem achieves state-of-the-art performance on MNIST and is considerably betterthan a convolutional net at recognizing highly overlapping digits. To achieve theseresults we use an iterative Routing -by-agreement mechanism: A lower-level capsuleprefers to send its output to higher level Capsules whose activity vectors have a bigscalar product with the prediction coming from the lower-level IntroductionHuman vision ignores irrelevant details by using a carefully determined sequence of fixation pointsto ensure that only a tiny fraction of the optic array is ever processed at the highest is a poor guide to}
of the vector to represent the properties of the entity1.We ensure that the length of the vector output of a capsule cannot exceed 1 by applying a non-linearity that leaves the orientation of the vector
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