  One of the prevailing trends in the machine- and deep-learning community is
to gravitate towards the use of increasingly larger models in order to keep
pushing the state-of-the-art performance envelope. This tendency makes access
to the associated technologies more difficult for the average practitioner and
runs contrary to the desire to democratize knowledge production in the field.
In this paper, we propose a framework for achieving improved memory efficiency
in the process of learning traditional neural networks by leveraging
inductive-bias-driven network design principles and layer-wise
manifold-oriented regularization objectives. Use of the framework results in
improved absolute performance and empirical generalization error relative to
traditional learning techniques. We provide empirical validation of the
framework, including qualitative and quantitative evidence of its effectiveness
on two standard image datasets, namely CIFAR-10 and CIFAR-100. The proposed
framework can be seamlessly combined with existing network compression methods
for further memory savings.
