The famous 2013 paper from Zeiler and Fergus "Visualizing and Understanding Convolutional Networks" proposes a method to understand the behavior of CNN using one (or more) DeConv networks coupled with the original CNN.
The DeConv Nets used employs a set of unpooling and deconvolutional layers to reconstruct the features in the input image that are responsible for the activation of a given feature map in a given layer.
These, however, use "max location switches" to reverse the max-pooling operation. These are, if I'm correct, pooling layers who applies an
ArgMax operation, allowing to map which are the positions the pooled maxima come from.
PoolingLayer does not accept
Is it possible to workaround this limitation and extract the "max location switches" in another way? Or is there any other technique applicable in Mathematica to produce a visualization similar to the one proposed by Zeiler and Fergus to understand which are the features that activate a give layer?