轻量级深度胶囊网络，提升复杂分类任务精度与效率
DL-CapsNet: A Deep and Light Capsule Network
- 多层胶囊结构结合摘要层，降低参数量
- 在高类别复杂数据上准确率优于传统CNN
- 适合需要高效推理的图像分类场景
胶囊网络（CapsNet）是具有前景的分类器，可能成为基于卷积神经网络（CNN）分类器的替代方案。相比CNN，CapsNet在处理重叠类别和仿射变换图像时更具优势。本文提出一种深层变体DL-CapsNet，包含多个胶囊层，并设计胶囊摘要层以减少参数数量。该模型在保持高准确率的同时，参数量少、训练和推理速度快，能够有效处理类别众多的复杂数据集。
原文摘要 · Abstract (English)
Capsule Network (CapsNet) is among the promising classifiers and a possible successor of the classifiers built based on Convolutional Neural Network (CNN). CapsNet is more accurate than CNNs in detecting images with overlapping categories and those with applied affine transformations. In this work, we propose a deep variant of CapsNet consisting of several capsule layers. In addition, we design the Capsule Summarization layer to reduce the complexity by reducing the number of parameters. DL-CapsNet, while being highly accurate, employs a small number of parameters and delivers faster training and inference. DL-CapsNet can process complex datasets with a high number of categories.
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