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.

翻译：本文提出点云网络（Point Cloud Network, PCN）架构，一种深度学习网络中线性层的新型实现方式，并基于实证证据倡导在神经网络线性层中优先采用该架构以替代多层感知机（MLP）。我们训练了包括原始AlexNet在内的多个模型，分别使用MLP与PCN架构进行线性层的直接对比（Krizhevsky et al., 2012）。关键实验结果涵盖模型参数量以及针对CIFAR-10和CIFAR-100数据集的top-1测试准确率（Krizhevsky, 2009）。AlexNet-PCN16作为与AlexNet等价的PCN变体，在保持相近的测试准确率的同时，其线性层参数量减少了99.5%。所有训练均在云RTX 4090 GPU上完成，并借助PyTorch实现模型构建与训练。本文提供完整代码以供相关实验复现。