I am going through Dilated Residual Network blog post. In this, Under
2.Multi-scale Context aggregation heading, author mentioned this.
The last one is the 1×1 convolutions for mapping the number of channels to be the same as the input one. Therefore, the input and the output has the same number of channels. And it can be inserted to different kinds of convolutional neural networks.
I thought, we decide number of channels in the next layer and kernels will be initialized randomly. These kernels shape is decided by us which are 1x1 or 3x3 etc., So, what did author mean when he said, 1x1 convolutions for mapping the number of channels to be the same as the input one.When, Even if its 2x2 convolutional kernel, number of channels are not changed.