机器学习新手在虚拟现实中使用卷积神经网络可视化

Nadine Meissler, Annika Wohlan, N. Hochgeschwender, A. Schreiber
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引用次数: 13

摘要

软件系统和组件越来越多地基于机器学习方法,如卷积神经网络(cnn)。因此,越来越多的普通程序员和机器学习新手需要了解这些算法的一般功能。然而,由于神经网络本质上是复杂的,因此需要新颖的表示方法来实现对其功能的快速访问。为此,我们研究了cnn如何在虚拟现实(VR)中可视化,因为它提供了通过沉浸和存在等效果将用户集中在内容上的机会。通过第一次探索性研究,我们证实了我们的可视化方法既直观易用,又有助于学习。此外,由于不同寻常的虚拟环境,用户表示学习的动机增加了。基于我们的发现,我们提出了一项后续研究,专门比较虚拟可视化方法与传统桌面可视化方法的好处。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Visualization of Convolutional Neural Networks in Virtual Reality for Machine Learning Newcomers
Software systems and components are increasingly based on machine learning methods, such as Convolutional Neural Networks (CNNs). Thus, there is a growing need for common programmers and machine learning newcomers to understand the general functioning of these algorithms. However, as neural networks are complex in nature, novel presentation means are required to enable rapid access to the functionality. For that purpose, we examine how CNNs can be visualized in Virtual Reality (VR), as it offers the opportunity to focus users on content through effects such as immersion and presence. With a first exploratory study, we confirmed that our visualization approach is both intuitive to use and conductive to learning. Moreover, users indicated an increased motivation to learning due to the unusual virtual environment. Based on our findings, we propose a follow-up study that specifically compares the benefits of a virtual visualization approach to a traditional desktop visualization.
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