  Following recent advances in morphological neural networks, we propose to
study in more depth how Max-plus operators can be exploited to define
morphological units and how they behave when incorporated in layers of
conventional neural networks. Besides showing that they can be easily
implemented with modern machine learning frameworks , we confirm and extend the
observation that a Max-plus layer can be used to select important filters and
reduce redundancy in its previous layer, without incurring performance loss.
Experimental results demonstrate that the filter selection strategy enabled by
a Max-plus is highly efficient and robust, through which we successfully
performed model pruning on different neural network architectures. We also
point out that there is a close connection between Maxout networks and our
pruned Max-plus networks by comparing their respective characteristics. The
code for reproducing our experiments is available online.
