Why zero padding is not used in linear convolution

Zero-padding for cross correlation is used to not mix convolution results (due to circular convolution). The full result of a linear convolution is longer than either of the two input vectors.

1. What is zero padding linear convolution?
2. Is zero padding is mandatory for both linear and circular convolution?
3. Does zero padding affect CNN?
4. What does zero padding do while solving linear convolution using circular convolution?

What is zero padding linear convolution?

Zero padding is a technique typically employed to make the size of the input sequence equal to a power of two. In zero padding, you add zeros to the end of the input sequence so that the total number of samples is equal to the next higher power of two.

Is zero padding is mandatory for both linear and circular convolution?

The linear convolution of an N-point vector, x , and an L-point vector, y , has length N + L - 1. For the circular convolution of x and y to be equivalent, you must pad the vectors with zeros to length at least N + L - 1 before you take the DFT.

Does zero padding affect CNN?

Padding is a term relevant to convolutional neural networks as it refers to the amount of pixels added to an image when it is being processed by the kernel of a CNN. For example, if the padding in a CNN is set to zero, then every pixel value that is added will be of value zero.

What does zero padding do while solving linear convolution using circular convolution?

what does zero padding do while solving linear convulation using circular convulation? Zero-padding avoids time-domain aliasing and make the circular convolution behave like linear convolution.