  We introduce a simple and effective method for regularizing large
convolutional neural networks. We replace the conventional deterministic
pooling operations with a stochastic procedure, randomly picking the activation
within each pooling region according to a multinomial distribution, given by
the activities within the pooling region. The approach is hyper-parameter free
and can be combined with other regularization approaches, such as dropout and
data augmentation. We achieve state-of-the-art performance on four image
datasets, relative to other approaches that do not utilize data augmentation.
