深度可分离卷积对胶囊网络的一种改进

从图像背景可以挑战其性能的角度来看，胶囊网络在计算机视觉方面面临着一个关键问题，尽管它们在训练数据上学得很好。在这项工作中，我们建议通过用深度可分离卷积代替标准卷积来改进胶囊网络的体系结构。..

An Improvement for Capsule Networks using Depthwise Separable Convolution

Capsule Networks face a critical problem in computer vision in the sense that the image background can challenge its performance, although they learn very well on training data. In this work, we propose to improve Capsule Networks' architecture by replacing the Standard Convolution with a Depthwise Separable Convolution.This new design significantly reduces the model's total parameters while increases stability and offers competitive accuracy. In addition, the proposed model on $64\times64$ pixel images outperforms standard models on $32\times32$ and $64\times64$ pixel images. Moreover, we empirically evaluate these models with Deep Learning architectures using state-of-the-art Transfer Learning networks such as Inception V3 and MobileNet V1. The results show that Capsule Networks perform equivalently against Deep Learning models. To the best of our knowledge, we believe that this is the first work on the integration of Depthwise Separable Convolution into Capsule Networks.