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Squashing Function

Non-linear activation function specific to capsule networks that compresses the length of activation vectors between 0 and 1 while preserving their spatial direction.

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Capsule Activation Vector

Multidimensional vector representation where the length indicates the probability of the entity's existence and the orientation encodes its instantiation parameters.

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Transformation Matrix

Weight matrix learned by each lower-level capsule to predict the output of higher-level capsules by transforming the spatial properties of the entity.

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Primary Capsule Layer

First layer of capsules that converts convolutional feature maps into multidimensional capsule vectors encoding low-level local properties.

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Coupling Coefficients

Scalar coefficients iteratively adjusted that determine the influence of each lower-level capsule on each higher-level capsule in the routing process.

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Entity Pose

Set of explicit spatial properties such as position, orientation, scale, and deformation encoded in the capsule vector's orientation.

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Hierarchical Representation

Organizational structure where low-level capsules detect simple primitives and high-level capsules combine these detections into complex entities.

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Capsule Prediction

Vector obtained by multiplying a lower-level capsule's vector by its transformation matrix to predict the expected output of a higher-level capsule.

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Coefficient d'accord

Produit scalaire entre la prédiction d'une capsule inférieure et la sortie actuelle d'une capsule supérieure mesurant leur compatibilité spatiale.

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Overlap pooling

Technique alternative au max-pooling qui préserve les informations spatiales en utilisant des capsules pour gérer le chevauchement des caractéristiques détectées.

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