  The remarkable successes of deep learning models across various applications
have resulted in the design of deeper networks that can solve complex problems.
However, the increasing depth of such models also results in a higher storage
and runtime complexity, which restricts the deployability of such very deep
models on mobile and portable devices, which have limited storage and battery
capacity. While many methods have been proposed for deep model compression in
recent years, almost all of them have focused on reducing storage complexity.
In this work, we extend the teacher-student framework for deep model
compression, since it has the potential to address runtime and train time
complexity too. We propose a simple methodology to include a noise-based
regularizer while training the student from the teacher, which provides a
healthy improvement in the performance of the student network. Our experiments
on the CIFAR-10, SVHN and MNIST datasets show promising improvement, with the
best performance on the CIFAR-10 dataset. We also conduct a comprehensive
empirical evaluation of the proposed method under related settings on the
CIFAR-10 dataset to show the promise of the proposed approach.
