Convolution kernel

1. What does convolution kernel do?
2. What is kernel in convolutional layer?
3. How do you find the kernel for convolution?
4. Why is kernel flipped in convolution?

What does convolution kernel do?

A convolution kernel defines how a filter alters the pixel values in a grayscale image. The convolution kernel is a 2D structure whose coefficients define how the filtered value at each pixel is computed.

What is kernel in convolutional layer?

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.

How do you find the kernel for convolution?

The kernel needs to have the same depth as the input. You calculate the convolution of each channel in the kernel with each corresponding channel in the image. Essentially, you need to perform the 2D convolution operation three times over, and then you sum up the results to get the final kernel output.

Why is kernel flipped in convolution?

Basically it's because time goes along the x axis with the small time values on the left and the big (later) time values on the right. So if you start shifting in, you're having the big time values hit your signal first, which is not right (causal). So you have to flip it to make the small time values shift in first.