Deep Learning allows us to build powerful models to solve problems like image classification, time series prediction, natural language processing, etc. This is achieved at the cost of huge amounts of storage and processing requirements which are sometimes not possible in machines with limited resources. In this paper, we compare different methods which tackle this problem with network pruning. Selected few pruning methodologies from the deep learning literature were implemented to display their results. Modern neural architectures have a combination of different layers like convolutional layers, pooling layers, dense layers, etc. We compare pruning techniques for dense layers (such as unit/neuron pruning, and weight Pruning), and convolutional layers as well (using L1 norm, taylor expansion of loss to determine importance of convolutional filters, and Variable Importance in Projection using Partial Least Squares) for the image classification task. This study aims to ease the overhead in terms of optimization of the model for academic, as well as commercial, use of deep neural networks.
Neurons vs Weights Pruning in Artificial Neural Networks