  This work aims to address the long-established problem of learning
diversified representations. To this end, we combine information-theoretic
arguments with stochastic competition-based activations, namely Stochastic
Local Winner-Takes-All (LWTA) units. In this context, we ditch the conventional
deep architectures commonly used in Representation Learning, that rely on
non-linear activations; instead, we replace them with sets of locally and
stochastically competing linear units. In this setting, each network layer
yields sparse outputs, determined by the outcome of the competition between
units that are organized into blocks of competitors. We adopt stochastic
arguments for the competition mechanism, which perform posterior sampling to
determine the winner of each block. We further endow the considered networks
with the ability to infer the sub-part of the network that is essential for
modeling the data at hand; we impose appropriate stick-breaking priors to this
end. To further enrich the information of the emerging representations, we
resort to information-theoretic principles, namely the Information Competing
Process (ICP). Then, all the components are tied together under the stochastic
Variational Bayes framework for inference. We perform a thorough experimental
investigation for our approach using benchmark datasets on image
classification. As we experimentally show, the resulting networks yield
significant discriminative representation learning abilities. In addition, the
introduced paradigm allows for a principled investigation mechanism of the
emerging intermediate network representations.

Machine Learning;Machine Learning