Capsule Network Sparsity refers to the targeted reduction of active connections and neurons in capsule networks to avoid overfitting and increase efficiency.
In a capsule network, capsules encode feature vectors, and sparsity mechanisms ensure that only relevant capsules remain active while irrelevant ones are suppressed.
This can be achieved through techniques such as L1 regularization, thresholding, or special activation functions that promote a natural selection of the most important features.
The sparse representations improve interpretability and reduce computational effort, which is especially beneficial in large networks.
Additionally, sparsity supports robustness against noise and promotes generalization to new data.