cnn-parameter-sharing-translation-invariance

IN premise — entries/2026/06/21/wiki-Convolutional_neural_network-chunk-3.md

Created 2026-06-21T09:55:49+00:00

Parameter sharing in convolutional layers (all neurons in a depth slice share weights and bias) reduces free parameters and provides translation invariance

Dependents

These beliefs depend on this one:

• IN cnn-efficiency-from-inductive-biases — CNNs achieve dramatic parameter efficiency through two complementary inductive biases — local connectivity restricts each neuron's receptive field, and weight sharing forces all spatial positions to use identical filters — reducing parameters by orders of magnitude.
• IN weight-sharing-is-universal-efficiency-principle — Weight sharing is a universal efficiency principle appearing independently across architectures — CNN parameter sharing reduces weights by orders of magnitude (25 vs 10,000 for a 5×5 kernel), transformer weight tying halves embedding parameters, and the principle generalizes to any architecture where symmetry in the data structure (spatial, positional) can be exploited to reduce free parameters without reducing expressiveness.