\(
\newcommand{\P}[]{\unicode{xB6}}
\newcommand{\AA}[]{\unicode{x212B}}
\newcommand{\empty}[]{\emptyset}
\newcommand{\O}[]{\emptyset}
\newcommand{\Alpha}[]{Α}
\newcommand{\Beta}[]{Β}
\newcommand{\Epsilon}[]{Ε}
\newcommand{\Iota}[]{Ι}
\newcommand{\Kappa}[]{Κ}
\newcommand{\Rho}[]{Ρ}
\newcommand{\Tau}[]{Τ}
\newcommand{\Zeta}[]{Ζ}
\newcommand{\Mu}[]{\unicode{x039C}}
\newcommand{\Chi}[]{Χ}
\newcommand{\Eta}[]{\unicode{x0397}}
\newcommand{\Nu}[]{\unicode{x039D}}
\newcommand{\Omicron}[]{\unicode{x039F}}
\DeclareMathOperator{\sgn}{sgn}
\def\oiint{\mathop{\vcenter{\mathchoice{\huge\unicode{x222F}\,}{\unicode{x222F}}{\unicode{x222F}}{\unicode{x222F}}}\,}\nolimits}
\def\oiiint{\mathop{\vcenter{\mathchoice{\huge\unicode{x2230}\,}{\unicode{x2230}}{\unicode{x2230}}{\unicode{x2230}}}\,}\nolimits}
\)
Capsule network try to perform the inverse of the rendering process.
See also Wikipedia: Capsule neural network
Dynamic Routing Between Capsules (NIPS 2017)
Capsule
"A capsule is a group of neurons whose activity vector represents the instantiation
parameters of a specific type of entity such as an object or an object part." - Nips 2017 Paper
- Rather than a neuron outputting one value, each capsule outputs a vector
- Each capsule then predicts the next layer's output
- Done through multiplication with a learned transformation matrix
Capsule Routing
- Todo
- Add Procedure 1 from the figure
- Here \(\displaystyle b_{i,j}\) is a routing weight for link between capsules in the current and next layer
CapsNet
Resources
Posts
Videos
