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2014

Fractional Max-Pooling

Graham, Benjamin

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Convolutional networks almost always incorporate some form of spatial pooling, and very often it is alpha times alpha max-pooling with alpha=2.

Max-pooling act on the hidden layers of the network, reducing their size by an integer multiplicative factor alpha.
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.

Fractional Max-Pooling · Around

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