Understanding Hinton’s Capsule Networks. Part I: Intuition.

19 bookmarks. First posted by kleinsound 15 days ago.

Last week, Geoffrey Hinton and his team published two papers that introduced a completely new type of neural network based on so-called capsules. In addition to that, the team published an algorithm…

7 days ago by pmigdal

A good explanation of Hinton's Capsule Networks.

Internal data representation of a convolutional neural network does not take into account important spacial hierarchies between simple and complex objects. CNNs use max pooling to address this. However, max pooling loses information. The pooling operation used in convolutional neural networks is a big mistake and the fact that it works so well is a disaster. One way to take the spatial relationships into account is to use an inverse graphics approach to create a model that does not depend on the view angle. A 4D (hierarchical) pose matrix does this.

See also: the succeeding articles providing more detail.

machinelearning
deeplearning
hinton
capsules
capsuletheory
capnet
cnn
cnns
convnet
posematrix
Internal data representation of a convolutional neural network does not take into account important spacial hierarchies between simple and complex objects. CNNs use max pooling to address this. However, max pooling loses information. The pooling operation used in convolutional neural networks is a big mistake and the fact that it works so well is a disaster. One way to take the spatial relationships into account is to use an inverse graphics approach to create a model that does not depend on the view angle. A 4D (hierarchical) pose matrix does this.

See also: the succeeding articles providing more detail.

7 days ago by drmeme

Understanding Hinton’s Capsule Networks. Part I: Intuition.

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9 days ago by demon386

1. Introduction Last week, Geoffrey Hinton and his team published two papers that introduced a completely new type of neural network based on so-called capsules…

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9 days ago by rogerhsueh

10 days ago by geetarista