Fractional Max-Pooling

Convolutional networks almost always incorporate some form of spatial pooling, and very often it is max-pooling with = 2. Max-pooling act on the hidden layers of the network, reducing their size by an integer multiplicative factor . 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 is allowed to take non-integer values. Our version of maxpooling is stochastic as there are lots of different ways of constructing suitable pooling regions. We find that our form of fractional max-pooling reduces overfitting on a variety of datasets: for instance, we improve on the state of the art for CIFAR-100 without even using dropout.