What is weight sharing in CNNs and why is it important?

Weight sharing means the same filter is applied across the entire image, reducing parameters and improving generalization.

In CNNs, a single filter (kernel) is applied across all spatial locations of an image. This is called weight sharing. It significantly reduces the number of parameters compared to fully connected networks. It also allows the model to detect patterns regardless of their position in the image, making it translation invariant.