Capsule neural network: Difference between revisions

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{{Short description|Type of artificial neural network}}

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”"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|last1=Hinton|first1=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 |chapter=Transforming Auto-Encoders |date=2011-06-14|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|s2cid=6138085 }}</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|last1=Hinton|first1=Geoffrey E|last2=Ghahramani|first2=Zoubin|last3=Teh|first3=Yee Whye|date=2000|publisher=MIT Press|editor-last=Solla|editor-first=S. A.|editor-link=Sara Solla|pages=463–469|editor-last2=Leen|editor-first2=T. K.|editor-last3=Müller|editor-first3=K.}}</ref>

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== Pooling ==

Capsnets reject the [[convolutional neural network#Pooling layer|pooling layer]] strategy of conventional CNNs that reduces the amount of detail to be processed at the next higher layer. Pooling allows a degree of translational invariance (it can recognize the same object in a somewhat different ___location) and allows a larger number of feature types to be represented. Capsnet proponents argue that pooling:<ref name=":1"/>

* violates biological shape perception in that it has no intrinsic coordinate frame;

* provides invariance (discarding positional information) instead of equivariance (disentangling that information);

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Capsnets are hierarchical, in that each lower-level capsule contributes significantly to only one higher-level capsule.<ref name=":1"/>

However, replicating learned knowledge remains valuable. To achieve this, a capsnet's lower layers are [[convolution]]al, including hidden capsule layers. Higher layers thus cover larger regions, while retaining information about the precise position of each object within the region. For low level capsules, ___location information is “place"place-coded”coded" according to which capsule is active. Higher up, more and more of the positional information is [[Neural coding|rate-coded]] in the capsule's output vector. This shift from place-coding to rate-coding, combined with the fact that higher-level capsules represent more complex objects with more degrees of freedom, suggests that capsule dimensionality increases with level.<ref name=":1"/>

== Human vision ==

Human vision examines a sequence of focal points (directed by [[saccade]]s), processing only a fraction of the scene at its highest resolution. Capsnets build on inspirations from [[cortical minicolumn]]s (also called cortical microcolumns) in the [[cerebral cortex]]. A minicolumn is a structure containing 80-120 neurons, with a diameter of about 28-40&nbsp;µmμm, spanning all layers in the cerebral cortex. All neurons in the larger minicolumns have the same [[receptive field]], and they output their activations as [[action potential]]s or spikes.<ref name=":1"/> Neurons within the microcolumn receive common inputs, have common outputs, are interconnected and may constitute a fundamental computational unit of the [[cerebral cortex]].<ref>{{Cite web|url=http://www.physics.drexel.edu/~ccruz/micros/research.html|title=Microcolumns in the Brain|website=www.physics.drexel.edu|access-date=2017-12-31|archive-date=2018-05-27|archive-url=https://web.archive.org/web/20180527140322/http://www.physics.drexel.edu/%7Eccruz/micros/research.html|url-status=dead}}</ref>

Capsnets explore the intuition that the human visual system creates a [[Parse tree|tree]]-like structure for each focal point and coordinates these trees to recognize objects. However, with capsnets each tree is "carved" from a fixed network (by adjusting coefficients) rather than assembled on the fly.<ref name=":1"/>

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* {{Citation|title=Pytorch code: Capsule Routing via Variational Bayes | date=February 2020|url=https://github.com/fabio-deep/Variational-Capsule-Routing|access-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|access-date=2017-12-08}}

* {{youtubeYouTube|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}}

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* {{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|access-date=2017-12-08}}

* {{Cite web|url=https://openreview.net/pdf?id=HJWLfGWRb|title=MATRIX CAPSULES WITH EM ROUTING|last1=Hinton|first1=Geoffrey|last2=Sabour|first2=Sara|last3=Frosst|first3=Nicholas|date=November 2017}}

* {{youtubeYouTube|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|access-date=2017-12-08}}

*{{Cite web|first=Fangyu|last=Cai|date=2020-12-18|title='We Can Do It' — Geoffrey Hinton and UBC, UT, Google & UVic Team Propose Unsupervised Capsule…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|last1=Sun|first1=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}}