Towards Generalized Entropic Sparsification for Convolutional Neural Networks

TL;DR

This paper presents a scalable, data-driven pruning method for CNNs based on entropic relaxation, effectively reducing network size with minimal accuracy loss across multiple benchmarks.

Contribution

Introduces a novel entropic relaxation-based pruning technique for CNNs that is computationally scalable and effective in achieving high sparsity with minimal accuracy loss.

Findings

Achieved 55-84% sparsity on MNIST with 0.1-0.5% accuracy loss

Achieved 73-89% sparsity on CIFAR-10 with 0.1-0.5% accuracy loss

Validated method on multiple architectures and datasets

Abstract

Convolutional neural networks (CNNs) are reported to be overparametrized. The search for optimal (minimal) and sufficient architecture is an NP-hard problem as the hyperparameter space for possible network configurations is vast. Here, we introduce a layer-by-layer data-driven pruning method based on the mathematical idea aiming at a computationally-scalable entropic relaxation of the pruning problem. The sparse subnetwork is found from the pre-trained (full) CNN using the network entropy minimization as a sparsity constraint. This allows deploying a numerically scalable algorithm with a sublinear scaling cost. The method is validated on several benchmarks (architectures): (i) MNIST (LeNet) with sparsity 55%-84% and loss in accuracy 0.1%-0.5%, and (ii) CIFAR-10 (VGG-16, ResNet18) with sparsity 73-89% and loss in accuracy 0.1%-0.5%.