<p>Zeiler and Fergus <a href="https://arxiv.org/pdf/1311.2901.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1311.2901.pdf</a> have a good picture showing kernel response to different parts of a picture. </p>

<p>Each kernel convolves over the image, so all the kernels (potentially) see all the pixels. Each of your 6 filters will "learn" a different feature. In the first layer, some will typically learn line features that look like lines (horizontal, vertical, diagonal) and some will learn colour blobs. In the next layer, these get combined. Pixels into edges into shapes.</p>

<p>It might help to look up Prewitt filters <a href="https://en.m.wikipedia.org/wiki/Prewitt_operator" rel="nofollow noreferrer">https://en.m.wikipedia.org/wiki/Prewitt_operator</a>
In this case, it is a single 3x3 kernel which convolves over the whole image and gives a feature map showing horizontal (or vertical) edges. You need one filter for horizontal and a different filter for vertical, but you can combine them to give both. In a neural network, the kernel values are learned from data but the feature maps at each layer are still produced by convolving the kernel over the input.</p>
What is the advantage of using multiples of the same filter in convolutional networks in deep learning
<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>