Capsule neural network: Difference between revisions

Revision as of 06:28, 25 February 2019

Revision as of 14:39, 25 February 2019

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A '''Capsule NeutralNeural Network''' ('''CapsNet''') is a machine learning system that is a type of [[artificial neural network]] (ANN) that can be used to better model hierarchical relationships. The approach is an attempt to more closely mimic biological neural organization.<ref name=":1" />

The idea is to add structures called capsules to a [[convolutional neural network]] (CNN), and to reuse output from several of those capsules to form more stable (with respect to various perturbations) representations for higher order capsules.<ref>{{Cite book|last=Hinton|first=Geoffrey E.|last2=Krizhevsky|first2=Alex|last3=Wang|first3=Sida D.|date=2011-06-14|title=Transforming Auto-Encoders|journal=Artificial Neural Networks and Machine Learning – ICANN 2011|volume=6791|series=Lecture Notes in Computer Science|language=en|publisher=Springer, Berlin, Heidelberg|pages=44–51|doi=10.1007/978-3-642-21735-7_6|isbn=9783642217340|citeseerx=10.1.1.220.5099}}</ref> The output is a vector consisting of the [[Realization (probability)|probability of an observation]], and a [[Pose (computer vision)|pose for that observation]]. This vector is similar to what is done for example when doing&nbsp;''classification with localization''&nbsp;in CNNs.

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== GeographyHistory ==

In 2000 [[Geoffrey Hinton]] et. al. described an imaging system that combined segmentation and recognition into a single inference process using [[Parse tree|parse trees]]. So-called credibility networks described the joint distribution over the latent variables and over the possible parse trees. That system proved useful on the [[MNIST database|MNIST]] handwritten digit database.<ref name=":0" />