Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

"Honestly, I am a bit confused with channels and filters … are they
the same or not?! How about kernels?"
This is a bit confusing indeed—let’s try to organize our thoughts about filters,
kernels, and channels.
This is what we have seen so far:
1. Every filter has as many channels as the image it is convolving (input).
2. Each channel of the filter / kernel is a small square matrix (that is being
convolved over the corresponding input channel).
3. A convolution produces as many channels as there are filters (output).
For example, we may have an RGB image as input (three channels) and use two
filters (each having three channels of its own to match the input (1)) to produce
two channels as output (3).
But things can get very messy if we start using "filter" to designate other things:
• Each channel of a filter / kernel is often referred to as a "filter" itself.
• Each channel of the output produced by convolving the filter over the input
image is often referred to as a "filter" too.
In the example, we would then have six "filters" (instead of two filters with three
channels each), and we would produce two "filters" as output (instead of two
channels). This is confusing!
We’re avoiding these messy definitions here, so we’re using channel
concatenation (or even better, stacking) instead of the confusing "filter
concatenation."
Having cleared that up, we can return to the Inception module itself. As we’ve seen,
the regular version simply stacks the output channels of convolutions with
different filter / kernel sizes. What about the other version?
It also does that, but the Inception module with dimension-reduction layers uses
1x1 convolutions to:
• reduce the number of input channels for both 3x3 and 5x5 convolution
branches; and
528 | Chapter 7: Transfer Learning