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]:
'For figure 4, I think it's important to note that your evaluation
method only returns a low number if *all* of the units in a layer are
invariant to the studied transformation.'

I agree and will add a sentence to the paper noting this.
I actually thought about whether there is a better way to produce such
plots than what was done in prior work  as well. Your suggestions
seems solid and would be a nice addition to the whole layer analysis
that is depicted in Figure 4. I will try to get around to implementing it
this way (although this might have to wait a few days).