Fractional Max-Pooling

TL;DR

This paper introduces a fractional max-pooling method allowing non-integer pooling ratios, which enhances convolutional network performance by reducing overfitting and increasing invariance, outperforming traditional max-pooling techniques.

Contribution

The authors propose a novel stochastic fractional max-pooling technique that generalizes standard max-pooling with non-integer ratios, improving model robustness and accuracy.

Findings

Reduces overfitting across multiple datasets

Improves state-of-the-art results on CIFAR-100

Enhances invariance to translations and distortions

Abstract

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. We have formulated a fractional version of max-pooling where alpha 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 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.