I've read explanation of convolution and understand it to some extent. Can somebody help me understand how this operation relates to convolution in Convolutional Neural Nets? Is filter like function g which applies weight?

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    $\begingroup$ ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets $\endgroup$
    – Hobbes
    Commented Jun 27, 2017 at 14:27
  • $\begingroup$ That's exactly what I'm reading and I see from there that convolution in CNN is a matrix operation. And "functional" convolution is never used there? So this is just 2 different operations with the same name? $\endgroup$
    – noname7619
    Commented Jun 27, 2017 at 14:29
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    $\begingroup$ Possibly the difference you are seeing is between discrete and continuous views of convolution - it is essentially the same operation, but has to be performed differently in those two different spaces. CNNs use discrete convolutions. And they only do it because it is a convenient way to express the maths of the connections (this applies in both directions - it is a mathematical convenience given the design, and likely that one reason this design is popular because it maps neatly to a well-understood function already used in signal processing) $\endgroup$ Commented Jun 27, 2017 at 14:34

3 Answers 3


Using the notation from the wikipedia page, the convolution in a CNN is going to be the kernel $g$ of which we will learn some weights in order to extract the information we need and then perhaps apply an activation function.

Discrete convolutions

From the wikipedia page the convolution is described as

$(f * g)[n] = \sum_{m=-\inf}^{\inf} f[m]g[n-m]$

For example assuming $a$ is the function $f$ and $b$ is the convolution function $g$,

enter image description here

To solve this we can use the equation first we flip the function $b$ vertically, due to the $-m$ that appears in the equation. Then we will calculate the summation for each value of $n$. Whilst changing $n$, the original function does not move, however the convolution function is shifted accordingly. Starting at $n=0$,

$c[0] = \sum_m a[m]b[-m] = 0 * 0.25 + 0 * 0.5 + 1 * 1 + 0.5 * 0 + 1 * 0 + 1 * 0 = 1$

$c[1] = \sum_m a[m]b[-m] = 0 * 0.25 + 1 * 0.5 + 0.5 * 1 + 1 * 0 + 1 * 0 = 1$

$c[2] = \sum_m a[m]b[-m] = 1 * 0.25 + 0.5 * 0.5 + 1 * 1 + 1 * 0 + 1 * 0 = 1.5$

$c[3] = \sum_m a[m]b[-m] = 1 * 0 + 0.5 * 0.25 + 1 * 0.5 + 1 * 1 = 1.625$

$c[4] = \sum_m a[m]b[-m] = 1 * 0 + 0.5 * 0 + 1 * 0.25 + 1 * 0.5 + 0 * 1 = 0.75$

$c[5] = \sum_m a[m]b[-m] = 1 * 0 + 0.5 * 0 + 1 * 0 + 1 * 0.25 + 0 * 0.5 * 0 * 1 = 0.25$

As you can see that is exactly what we get on the plot $c[n]$. So we shifted around the function $b[n]$ over the function $a[n]$.

2D Discrete Convolution

For example, if we have the matrix in green

enter image description here

with the convolution filter

enter image description here

Then the resulting operation is a element-wise multiplication and addition of the terms as shown below. Very much like the wikipedia page shows, this kernel (orange matrix) $g$ is shifted across the entire function (green matrix) $f$.

enter image description here

taken from the link that @Hobbes reference. You will notice that there is no flip of the kernel $g$ like we did for the explicit computation of the convolution above. This is a matter of notation as @Media points out. This should be called cross-correlation. However, computationally this difference does not affect the performance of the algorithm because the kernel is being trained such that its weights are best suited for the operation, thus adding the flip operation would simply make the algorithm learn the weights in different cells of the kernel to accommodate the flip. So we can omit the flip.


Yes they are related. As an example, consider Gaussian smoothing (en.wikipedia.org/wiki/Gaussian_blur) which is a convolution with a kernel of Gaussian values. A CNN learns the weights of filters (i.e. kernels), and thus can learn to perform smoothing if needed.


Although CNN stands for convolutional neural networks, what they do is named cross-correlation in mathematics and not convolution. Take a look at here.

Now, before moving on there is a technical comment I want to make about cross-correlation versus convolutions and just for the facts what you have to do to implement convolutional neural networks. If you reading different math textbook or signal processing textbook, there is one other possible inconsistency in the notation which is that, if you look at the typical math textbook, the way that the convolution is defined before doing the element Y's product and summing, there's actually one other step ...


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