This paper introduces the Point Cloud Network (PCN) architecture, a novel
implementation of linear layers in deep learning networks, and provides
empirical evidence to advocate for its preference over the Multilayer
Perceptron (MLP) in linear layers. We train several models, including the
original AlexNet, using both MLP and PCN architectures for direct comparison of
linear layers (Krizhevsky et al., 2012). The key results collected are model
parameter count and top-1 test accuracy over the CIFAR-10 and CIFAR-100
datasets (Krizhevsky, 2009). AlexNet-PCN16, our PCN equivalent to AlexNet,
achieves comparable efficacy (test accuracy) to the original architecture with
a 99.5% reduction of parameters in its linear layers. All training is done on
cloud RTX 4090 GPUs, leveraging pytorch for model construction and training.
Code is provided for anyone to reproduce the trials from this paper.

引入了 Point Cloud Network (PCN) 架构，通过对线性层的直接比较，提供经验证据证明 PCN 优于多层感知机 (MLP)。在 CIFAR-10 和 CIFAR-100 数据集的训练中，AlexNet-PCN16 在其线性层参数减少 99.5% 的同时，达到与原始架构相当的测试准确率。