Discrete convolution
Slides one signal over another, multiplying and summing at every offset. Reusing the same small kernel everywhere is what gives convolutional networks translation equivariance and their very small parameter count.
- The input signal, such as an image row.
- The kernel being slid across it.
- The output position.