Parameter sharing in convolutional neural networks provides two main advantages:

1. It significantly reduces the total number of parameters in the network, which helps mitigate the risk of overfitting.
2. It allows a feature detector (e.g., an edge detector) that is learned in one part of the image to be reused across the entire input volume.