Thank you for sharing this. If my understanding is correct, the research suggests that global pooling retains channel information while spatial information is collapsed (which is a bit counterintuitive to me). They further propose measures to improve translation invariance, which is the problem I am trying to get rid of.
In my example above on Bridge, what I am hoping to achieve is ‘translation equivariance’ along the horizontal direction if data is structured as it is image 1. This way, the output shall change if the red box in the first three rows move to the right - which is desirable.
I am also hoping to achieve ‘translation varaince’ along the vertical direction if the input data is structured as it is in the first image. This way, there is a clear distinction being made between the first, second, third, and fourth red boxes.