  Deep networks are an integral part of the current machine learning paradigm.
Their inherent ability to learn complex functional mappings between data and
various target variables, while discovering hidden, task-driven features, makes
them a powerful technology in a wide variety of applications. Nonetheless, the
success of these networks typically relies on the availability of sufficient
training data to optimize a large number of free parameters while avoiding
overfitting, especially for networks with large capacity. In scenarios with
limited training budgets, e.g., supervised tasks with limited labeled samples,
several generic and/or task-specific regularization techniques, including data
augmentation, have been applied to improve the generalization of deep
networks.Typically such regularizations are introduced independently of that
data or training scenario, and must therefore be tuned, tested, and modified to
meet the needs of a particular network. In this paper, we propose a novel
regularization framework that is driven by the population-level statistics of
the feature space to be learned. The regularization is in the form of a
\textbf{cooperating subnetwork}, which is an auto-encoder architecture attached
to the feature space and trained in conjunction with the primary network. We
introduce the architecture and training methodology and demonstrate the
effectiveness of the proposed cooperative network-based regularization in a
variety of tasks and architectures from the literature. Our code is freely
available at \url{https://github.com/riddhishb/CoopSubNet
