1412.6071v4.pdf


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Fractional Max-Pooling
Benjamin Graham
Dept of Statistics, University of Warwick, CV4 7AL, UK
b.graham@warwick.ac.uk
May 13, 2015
Abstract
Convolutional networks almost always incorporate some form of spatial
pooling, and very often it is α× α max-pooling with α = 2. Max-pooling
act on the hidden layers of the network, reducing their size by an integer
multiplicative factor α. The amazing by-product of discarding 75% of
your data is that you build into the network a degree of invariance with
respect to translations and elastic distortions. However, if you simply
alternate convolutional layers with max-pooling layers, performance is
limited due to the rapid reduction in spatial size, and the disjoint nature
of the pooling regions. We have formulated a fractional version of max-
pooling where α is allowed to take non-integer values. Our version of
max-pooling is stochastic as there are lots of different ways of constructing
suitable pooling regions. We find that our form


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