论文标题
CNN和通信系统之间的关系是什么？
What's the relationship between CNNs and communication systems?
论文作者
论文摘要
卷积神经网络（CNN）的解释性是计算机视觉领域的重要主题。近年来，该领域的工作通常采用一个成熟的模型来揭示CNN的内部机制，有助于彻底了解CNN。在本文中，我们认为可以通过比较通信系统和CNN来通过完全不同的解释来揭示CNN的工作机制。本文成功地获得了两者的模块之间的相应关系，并验证了与实验相应关系的合理性。最后，通过对神经网络的一些尖端研究的分析，我们发现这两个任务之间的固有关系可以有助于合理地解释这些研究，并帮助我们发现神经网络的正确研究方向。
The interpretability of Convolutional Neural Networks (CNNs) is an important topic in the field of computer vision. In recent years, works in this field generally adopt a mature model to reveal the internal mechanism of CNNs, helping to understand CNNs thoroughly. In this paper, we argue the working mechanism of CNNs can be revealed through a totally different interpretation, by comparing the communication systems and CNNs. This paper successfully obtained the corresponding relationship between the modules of the two, and verified the rationality of the corresponding relationship with experiments. Finally, through the analysis of some cutting-edge research on neural networks, we find the inherent relation between these two tasks can be of help in explaining these researches reasonably, as well as helping us discover the correct research direction of neural networks.
