Abstract
Channel pruning can effectively reduce both computational cost and memory footprint of the original network while keeping a comparable accuracy performance. Though great success has been achieved in channel pruning for 2D image-based convolutional networks (CNNs), existing works seldom extend the channel pruning methods to 3D point-based neural networks (PNNs). Directly implementing the 2D CNN channel pruning methods to PNNs undermine the performance of PNNs because of the different representations of 2D images and 3D point clouds as well as the network architecture disparity. In this paper, we proposed CP$^3$, which is a Channel Pruning Plug-in for Point-based network. CP$^3$ is elaborately designed to leverage the characteristics of point clouds and PNNs in order to enable 2D channel pruning methods for PNNs. Specifically, it presents a coordinate-enhanced channel importance metric to reflect the correlation between dimensional information and individual channel features, and it recycles the discarded points in PNN's sampling process and reconsiders their potentially-exclusive information to enhance the robustness of channel pruning. Experiments on various PNN architectures show that CP$^3$ constantly improves state-of-the-art 2D CNN pruning approaches on different point cloud tasks. For instance, our compressed PointNeXt-S on ScanObjectNN achieves an accuracy of 88.52% with a pruning rate of 57.8%, outperforming the baseline pruning methods with an accuracy gain of 1.94%.
Abstract (translated)
通道剪枝可以有效地降低原始网络的计算成本和内存 footprint，同时保持相同的精度性能。虽然对2D图像based卷积神经网络(CNN)的通道剪枝已经取得了巨大的成功，但现有的工作很少将通道剪枝方法扩展到3D点based神经网络(PNNs)。直接实现2D CNN通道剪枝方法到PNNs会削弱PNNs的性能，因为2D图像和3D点云的表示不同，以及网络架构差异大。在本文中，我们提出了CP$^3$，它是一个基于点based网络的通道剪枝插件。CP$^3$精心设计，利用点云和PNNs的特点，以便为PNNs实现2D通道剪枝方法。具体来说，它提出了一种坐标增强的通道重要性度量，以反映维度信息和个体通道特征之间的相关性，它回收了PNNs采样过程中丢弃的点，并重新考虑它们的可能 exclusive 信息，以增强通道剪枝的稳健性。对各种PNN架构的实验表明，CP$^3$ constantly improves the state-of-the-art 2D CNN通道剪枝方法在不同点云任务中的性能。例如，我们的压缩PointNeXt-S在扫描对象NN上的点云任务中，具有88.52%的精度，剪枝率为57.8%，比基准剪枝方法的精度提高1.94%。
URL
https://arxiv.org/abs/2303.13097
