Improving Deep Neural Networks with Probabilistic Maxout Units
Authors: Jost Tobias Springenberg, Martin Riedmiller
ICLR 2014

Abstract: 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).


Peer review:
[Review]:
We want to point out that an updated version of the paper is available on arXiv. The main changes are:
- Most minor comments of reviewers Anonymous f3f1 and Anonymous 4eb4 are adressed in the new version
- The experiments section has been changed to more clearly reflect statistically ties between maxout and probout in the results
- An additional experiment on invariance properties was added