title: What is the advantage of using multiples of the same filter in convolutional networks in deep learning
body: <p>What is the advantage of using multiples of the same filter in convolutional networks in deep learning?</p>
  
  <p>For example:
  We use 6 filter of size [5,5] at the first layer to scan the image data, which is a matrix of size [28,28].
  The question is why do we not use only a single filter of size [5,5] but use 6 or more of them. In the end they will scan the exact same pixels. I can see that the random weight might be different but DL model will adjust to it anyway.</p>
  
  <p>So, specifically what is the main advantage and purpose of using multiple filters of the same shape then in convnets?</p>