Capsule Network
Capsule Network (CapsNet) · Also known as: Kapsül Ağı (CapsNet), CapsNet, capsule net, dynamic routing network
A Capsule Network (CapsNet) is a deep learning architecture introduced by Sara Sabour, Nicholas Frosst and Geoffrey Hinton in 2017 that organises neurons as vectors (capsules) rather than scalar activations, so that spatial hierarchy and pose (orientation) information are encoded directly. It was proposed to overcome the fragility of convolutional networks to changes in viewpoint.
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When to use it
Suitable for classification and prediction on image or otherwise structured continuous-feature data where robustness to viewpoint and pose changes matters, with a reasonably large sample (at least about 500 observations). A GPU is recommended and the dynamic routing algorithm must be tuned. Below about 500 observations the routing cannot learn reliably and the model overfits; below roughly 200 a classical classifier such as random forest or an SVM is the better choice.
Strengths & limitations
- Encodes pose and spatial-hierarchy information that convolutional pooling discards, improving robustness to viewpoint changes.
- Models explicit part-to-whole relationships through routing-by-agreement.
- Vector capsules preserve entity properties (orientation, position) rather than collapsing them to a single activation.
- Requires a fairly large sample (about 500+); on smaller data the dynamic routing overfits and learns unreliably.
- Iterative dynamic routing is computationally demanding and a GPU is recommended.
- The routing algorithm introduces hyperparameters that must be tuned, adding training complexity.
Frequently asked
How is a capsule different from a normal neuron?
A normal neuron outputs a single scalar activation. A capsule outputs a vector whose length represents the probability that an entity is present and whose direction encodes that entity's properties, such as pose and orientation, so it carries far richer spatial information.
What is dynamic routing?
Dynamic routing-by-agreement is the algorithm that connects lower-level capsules to higher-level ones. Each lower capsule predicts the output of higher capsules, and coupling coefficients are refined iteratively so that predictions which agree reinforce each other, replacing the pooling step of a convolutional network.
How much data and compute do I need?
CapsNet needs a reasonably large dataset — at least about 500 observations — and a GPU is recommended because iterative routing is computationally demanding. Below roughly 200 observations a classical classifier such as random forest or an SVM is more appropriate.
Why use a capsule network instead of a CNN?
CapsNet was proposed specifically to address a CNN's fragility to viewpoint changes. By preserving pose and part-to-whole relationships rather than pooling them away, it can generalise better when objects appear in new orientations.
Sources
How to cite this page
ScholarGate. (2026, June 1). Capsule Network (CapsNet). ScholarGate. https://scholargate.app/en/deep-learning/capsule-network
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