What is kernel in CNN?

Question

Can anyone explain kernel in CNN?

Answer 1

In Convolutional neural network, the kernel is nothing but a filter that is used to extract the features from the images. The kernel is a matrix that moves over the input data, performs the dot product with the sub-region of input data, and gets the output as the matrix of dot products. Kernel moves on the input data by the stride value. If the stride value is 2, then kernel moves by 2 columns of pixels in the input matrix. In short, the kernel is used to extract high-level features like edges from the image.

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You can watch this video on How CNN works to understand more about kernel:

Answer 2

• A kernel is a central part of CNN that acts as a core matrix to extract features from the input.

• The kernel is typically a small matrix, such as 3*3 or 5*5 pixels.

• The kernel majorly operates in such a way that it slides over an input image, performs element-wise multiplication, and sums the result to produce a single value in the output feature map. This process is repeated across the entire input.

• In a Convolutional Neural Network, a kernel (filter) can also be termed a grid of weights that is applied to the input data to extract features.