Wouldn’t multiple filters in a convolutional layer learn the same parameter during training?

Based from what I have learned, we use multiple filters in a Conv Layer of a CNN to learn different feature detectors. But since these filters are applied similarly (i.e. slided and multiplied to regions of the input), wouldn’t they just learn the same parameters during training? Hence the use of multiple filters would be redundant?

Answer

I have found the answer to this question:
https://www.quora.com/Why-does-each-filter-learn-different-features-in-a-convolutional-neural-network

It says here: “… (optimization) algorithm finds that loss does not decrease if two filters have similar weights and biases, so it’ll eventually change one of the filter(‘s weights and biases) in order to reduce loss thereby learning a new feature.”

Thank you for the answers. Appreciate it 🙂

Attribution
Source : Link , Question Author : cjbayron , Answer Author : cjbayron

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