  We present a probabilistic variant of the recently introduced maxout unit.
The success of deep neural networks utilizing maxout can partly be attributed
to favorable performance under dropout, when compared to rectified linear
units. It however also depends on the fact that each maxout unit performs a
pooling operation over a group of linear transformations and is thus partially
invariant to changes in its input. Starting from this observation we ask the
question: Can the desirable properties of maxout units be preserved while
improving their invariance properties ? We argue that our probabilistic maxout
(probout) units successfully achieve this balance. We quantitatively verify
this claim and report classification performance matching or exceeding the
current state of the art on three challenging image classification benchmarks
(CIFAR-10, CIFAR-100 and SVHN).
