  In this paper, we introduce a new deep convolutional neural network (ConvNet)
module that promotes competition among a set of multi-scale convolutional
filters. This new module is inspired by the inception module, where we replace
the original collaborative pooling stage (consisting of a concatenation of the
multi-scale filter outputs) by a competitive pooling represented by a maxout
activation unit. This extension has the following two objectives: 1) the
selection of the maximum response among the multi-scale filters prevents filter
co-adaptation and allows the formation of multiple sub-networks within the same
model, which has been shown to facilitate the training of complex learning
problems; and 2) the maxout unit reduces the dimensionality of the outputs from
the multi-scale filters. We show that the use of our proposed module in typical
deep ConvNets produces classification results that are either better than or
comparable to the state of the art on the following benchmark datasets: MNIST,
CIFAR-10, CIFAR-100 and SVHN.
