While convolutional neural networks (CNN) have achieved impressive performance on
various classification/recognition tasks, they typically consist of a massive number of
parameters. This results in significant memory requirement as well as computational
overheads. Consequently, there is a growing need for filter-level pruning approaches for
compressing CNN based models that not only reduce the total number of parameters but
reduce the overall computation as well. We present a new min-max framework for filter-level …