Capsule neural network: Difference between revisions

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A '''Capsule Neural 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 capsules.<ref>{{Cite book|lastlast1=Hinton|firstfirst1=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 ''[[classification with localization]]'' in CNNs.

Among other benefits, capsnets address the "Picasso problem" in image recognition: images that have all the right parts but that are not in the correct spatial relationship (e.g., in a "face", the positions of the mouth and one eye are switched). For image recognition, capsnets exploit the fact that while viewpoint changes have nonlinear effects at the pixel level, they have linear effects at the part/object level.<ref name=":16">{{cite web|url=http://www.cedar.buffalo.edu/~srihari/CSE676/9.12%20CapsuleNets.pdf|title=Capsule Nets|last=Srihari|first=Sargur|publisher=[[University of Buffalo]]|access-date=2017-12-07}}</ref> This can be compared to inverting the rendering of an object of multiple parts.<ref name=":0">{{Cite book|url=http://papers.nips.cc/paper/1710-learning-to-parse-images.pdf|title=Advances in Neural Information Processing Systems 12|lastlast1=Hinton|firstfirst1=Geoffrey E|last2=Ghahramani|first2=Zoubin|last3=Teh|first3=Yee Whye|date=2000|publisher=MIT Press|editor-last=Solla|editor-first=S. A.|pages=463–469|editor-last2=Leen|editor-first2=T. K.|editor-last3=Müller|editor-first3=K.}}</ref>

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A dynamic routing mechanism for capsule networks was introduced by Hinton and his team in 2017. The approach was claimed to reduce error rates on [[MNIST database|MNIST]] and to reduce training set sizes. Results were claimed to be considerably better than a CNN on highly overlapped digits.<ref name=":1"/>

In Hinton's original idea one minicolumn would represent and detect one multidimensional entity.<ref>{{Citation|last=Meher Vamsi|title=Geoffrey Hinton Capsule theory|date=2017-11-15|url=https://www.youtube.com/watch?v=6S1_WqE55UQ|accessdateaccess-date=2017-12-06}}</ref><ref group="note" name=":0" />

== Transformations ==

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==References==

{{reflist|2|refs=

<ref name=":1">{{Cite arxiv|lastlast1=Sabour|firstfirst1=Sara|last2=Frosst|first2=Nicholas|last3=Hinton|first3=Geoffrey E.|date=2017-10-26|title=Dynamic Routing Between Capsules|eprint=1710.09829|class=cs.CV}}</ref>

}}

== External links ==

* {{Citation|title=Pytorch code: Capsule Routing via Variational Bayes | date=February 2020|url=https://github.com/fabio-deep/Variational-Capsule-Routing|accessdateaccess-date=2020-10-23}}

* {{Citation|title=A PyTorch implementation of the NIPS 2017 paper "Dynamic Routing Between Capsules"|date=2017-12-08|url=https://github.com/gram-ai/capsule-networks|publisher=Gram.AI|accessdateaccess-date=2017-12-08}}

* {{youtube|What's wrong with convolutional neural nets|id=rTawFwUvnLE}}

* {{Cite web|url=http://www.cedar.buffalo.edu/~srihari/CSE676|title=Deep Learning|website=www.cedar.buffalo.edu|access-date=2017-12-07}}

*{{Cite web|url=https://medium.freecodecamp.org/understanding-capsule-networks-ais-alluring-new-architecture-bdb228173ddc|title=Understanding Capsule Networks — AI's Alluring New Architecture|last=Bourdakos|first=Nick|date=2018-02-12|website=freeCodeCamp.org|access-date=2019-04-23}}

*{{Cite arxiv|lastlast1=Dai|firstfirst1=Jifeng|last2=Qi|first2=Haozhi|last3=Xiong|first3=Yuwen|last4=Li|first4=Yi|last5=Zhang|first5=Guodong|last6=Hu|first6=Han|last7=Wei|first7=Yichen|date=2017-03-17|title=Deformable Convolutional Networks|eprint=1703.06211|class=cs.CV}}

*{{Cite arxiv|lastlast1=De Brabandere|firstfirst1=Bert|last2=Jia|first2=Xu|last3=Tuytelaars|first3=Tinne|last4=Van Gool|first4=Luc|date=2016-05-31|title=Dynamic Filter Networks|eprint=1605.09673|class=cs.LG}}

* {{Citation|last=Guo|first=Xifeng|title=CapsNet-Keras: A Keras implementation of CapsNet in NIPS2017 paper "Dynamic Routing Between Capsules". Now test error ＝ 0.34%.|date=2017-12-08|url=https://github.com/XifengGuo/CapsNet-Keras|accessdateaccess-date=2017-12-08}}

* {{Cite web|url=https://openreview.net/pdf?id=HJWLfGWRb|title=MATRIX CAPSULES WITH EM ROUTING|lastlast1=Hinton|firstfirst1=Geoffrey|last2=Sabour|first2=Sara|last3=Frosst|first3=Nicholas|date=November 2017}}

* {{youtube|Hinton and Google Brain - Capsule Networks |id=x5Vxk9twXlE}}

* {{Citation|last=Liao|first=Huadong|title=CapsNet-Tensorflow: A Tensorflow implementation of CapsNet(Capsules Net) in Hinton's paper Dynamic Routing Between Capsules|date=2017-12-08|url=https://github.com/naturomics/CapsNet-Tensorflow|accessdateaccess-date=2017-12-08}}

*{{Cite web|first=Fangyu|last=Cai|date=2020-12-18|title=‘We'We Can Do It’It' — Geoffrey Hinton and UBC, UT, Google & UVic Team Propose Unsupervised Capsule…|url=https://medium.com/syncedreview/we-can-do-it-geoffrey-hinton-and-ubc-ut-google-uvic-team-propose-unsupervised-capsule-c1f2edb6b1e9|access-date=2021-01-18|website=Medium|language=en}}

* {{cite arxiv|lastlast1=Sun|firstfirst1=Weiwei|last2=Tagliasacchi|first2=Andrea|last3=Deng|first3=Boyang|last4=Sabour|first4=Sara|last5=Yazdani|first5=Soroosh|last6=Hinton|first6=Geoffrey|last7=Yi|first7=Kwang Moo|date=2020-12-08|title=Canonical Capsules: Unsupervised Capsules in Canonical Pose|class=cs.CV|eprint=2012.04718}}

[[Category:Artificial neural networks]]