Solved – Convolutional neural network with non-image input data

Can CNNs be used with input data which is not an image? The reason I'm asking is because the original image is often clipped in size because of border effects when doing the convolution.

But if the input is not an image, and I really need to use all the input data, is it possible to overcome the problem?

Convolutional networks work so well because they exploit an assumption about with weight sharing. This is why they only work with data where that assumption hold.

The assumption is a spatial one. It is best explained with a picture, where you do not care where exactly something is, which is sometimes called translational invariance.

As long as that assumption holds on your data, you can apply it. Other modalities are e.g. audio or (to some extent) text.

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