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In the framework of convolutional neural networks that lie at the heart ofdeep learning, downsampling is often performed with a max-pooling operationthat only retains the element with maximum activation, while completelydiscarding the information contained in other elements in a pooling region. Toaddress this issue, a novel pooling scheme, Ordinal Pooling Network (OPN), isintroduced in this work. OPN rearranges all the elements of a pooling region ina sequence and assigns different weights to these elements based upon theirorders in the sequence, where the weights are learned via the gradient-basedoptimisation. The results of our small-scale experiments on imageclassification task demonstrate that this scheme leads to a consistentimprovement in the accuracy over max-pooling operation. This improvement isexpected to increase in deeper networks, where several layers of pooling becomenecessary.

Ordinal Pooling Networks: For Preserving Information over Shrinking Feature Maps