Towards Optimal Structured CNN Pruning via Generative Adversarial Learning

TL;DR

This paper introduces a novel end-to-end structured pruning method for CNNs using generative adversarial learning to jointly prune various structures, achieving significant speedups with minimal accuracy loss.

Contribution

It proposes a unified, end-to-end approach for joint structured pruning of CNNs using a soft mask and GANs, outperforming existing multi-stage methods.

Findings

Achieves 3.7x speedup on ImageNet with ResNet-50.

Outperforms state-of-the-art pruning methods.

Effective on multiple datasets including MNIST, CIFAR-10, and ImageNet.

Abstract

Structured pruning of filters or neurons has received increased focus for compressing convolutional neural networks. Most existing methods rely on multi-stage optimizations in a layer-wise manner for iteratively pruning and retraining which may not be optimal and may be computation intensive. Besides, these methods are designed for pruning a specific structure, such as filter or block structures without jointly pruning heterogeneous structures. In this paper, we propose an effective structured pruning approach that jointly prunes filters as well as other structures in an end-to-end manner. To accomplish this, we first introduce a soft mask to scale the output of these structures by defining a new objective function with sparsity regularization to align the output of baseline and network with this mask. We then effectively solve the optimization problem by generative adversarial learning (GAL), which learns a sparse soft mask in a label-free and an end-to-end manner. By forcing more scaling factors in the soft mask to zero, the fast iterative shrinkage-thresholding algorithm (FISTA) can be leveraged to fast and reliably remove the corresponding structures. Extensive experiments demonstrate the effectiveness of GAL on different datasets, including MNIST, CIFAR-10 and ImageNet ILSVRC 2012. For example, on ImageNet ILSVRC 2012, the pruned ResNet-50 achieves 10.88\% Top-5 error and results in a factor of 3.7x speedup. This significantly outperforms state-of-the-art methods.