  Reducing network complexity has been a major research focus in recent years
with the advent of mobile technology. Convolutional Neural Networks that
perform various vision tasks without memory overhaul is the need of the hour.
This paper focuses on qualitative and quantitative analysis of reducing the
network complexity using an upper bound on the Vapnik-Chervonenkis dimension,
pruning, and quantization. We observe a general trend in improvement of
accuracies as we quantize the models. We propose a novel loss function that
helps in achieving considerable sparsity at comparable accuracies to that of
dense models. We compare various regularizations prevalent in the literature
and show the superiority of our method in achieving sparser models that
generalize well.
