Machine Learning – visualize and understand CNN with Mathematica

The famous 2013 article by Zeiler and Fergus "Visualization and Understanding of Convolution Networks" suggests a method to understand the behavior of CNN using one (or more) DeConv networks in conjunction with the original CNN.

The DeConv networks used use a set of unpooling and deconvolutional layers to reconstruct the features in the input image that are responsible for activating a particular feature map in a given layer.

These, however, use "Maximum location switchto undo the max pooling process, which, if I'm right, is to merge layers that are one argmax Operation, whereby the positions can be determined, from which the pooled maxima originate.

Unfortunately, PoolingLayer does not accept argmax as function Possibility.

Is it possible to circumvent this restriction and theMaximum location switchOr is there another technique that is applicable in Mathematica to produce a visualization similar to that proposed by Zeiler and Fergus to understand which features activate a given plane?