Weight sharing
= Weight sharing
Reuse of one learnable parameter in multiple computational positions. In a convolution this reduces parameter count and imposes <translation equivariance> away from boundary effects.
= Weight sharing
Reuse of one learnable parameter in multiple computational positions. In a convolution this reduces parameter count and imposes <translation equivariance> away from boundary effects.