Global Pooling, More than Meets the Eye: Position Information is Encoded Channel-Wise in CNNs

Published in ICCV, 2021

In this paper, we challenge the common assumption that collapsing the spatial dimensions of a 3D (spatial-channel) tensor in a convolutional neural network (CNN) into a vector via global pooling removes all spatial information. Specifically, we demonstrate that positional information is encoded based on the ordering of the channel dimensions, while semantic information is largely not.

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