# How do you prevent multiple kernels in a CNN from recognizing the same feature?

I've been reading Rosebrock's "Deep Learning for Computer Vision with Python", and he mentions that in a CNN, one of the layers is a set of $$K$$ kernels that each activate when they see a specific feature at a specific location.

I realize that this is likely a gross oversimplification, but assuming that the kernels all start out randomized, is there anything preventing many (or all) kernels converging to the same matrix? Without having much knowledge about this, I would guess that it's something with Linear Algebra using some measure to figure out how similar two matrices are and trying to tweak one of them to change?