Intuitive understanding of 1D, 2D, and 3D convolutions in convolutional neural networks

Question

Can anyone please clearly explain the difference between 1D, 2D, and 3D convolutions in convolutional neural networks (in deep learning) with the use of examples?

Answer 1

1. CNN 1D,2D, or 3D refers to convolution direction, rather than input or filter dimension.

2. For 1 channel input, CNN2D equals to CNN1D is kernel length = input length. (1 conv direction)