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I have been reading through Chapter 9 of www.deeplearningbbook.org, where convolutional networks are being described.

The following image represents the output of a 2D convolution, without kernel flipping.

enter image description here

The book goes on to describe this matrix as a Toeplitz matrix where,

for univariate discrete convolution, each row of the matrix is constrained to be equal to the row above shifted by one element.

I fully understand this statement since w, x, y and z are constants in their respective columns with shifting elements.

However, there is no mention of diagonal-constants which are a key feature of such matrices. As per Wikipedia (link above) and several other sources:

a Toeplitz matrix or diagonal-constant matrix, named after Otto Toeplitz, is a matrix in which each descending diagonal from left to right is constant.

Going back to the image above, does this mean that, for instance, aw == cx == gy? How can this be ensured when all elements are different?

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First of all, be assured that the kernel matrix (the 2x2 matrix in the figure) in CNN is not constrained to have its diagonal filled with a unique value, and is thus not a Toeplitz matrix.

In www.deeplearningbook.org, we can read:

"Discrete convolution can be viewed as multiplication by a matrix, but the matrix has several entries constrained to be equal to other entries"

This means that the global operation of passing a kernel on the input data of a CNN could be expressed as the multiplication of this input data by a matrix. i.e. by a large and sparse Toeplitz matrix.

Authors mention the illustrative simple case of univariate discrete convolution. In univariate discrete convolution, we would be applying a 1-D kernel matrix of length $m$ on 1-D input data of length $n$. Let us take n=10 and m=3. Doing the convolution could be done by multiplying the input vector data by a n*n matrix, whose diagonal would be composed of the 3 weights of the kernel, shifted of one column to the right at each new row.

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