利用神经元聚类解释神经网络的可视化分析技术

AI Pub Date : 2024-04-05 DOI:10.3390/ai5020023
Gülsüm Alicioğlu, Bo Sun
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引用次数: 0

摘要

深度学习(DL)模型在许多领域都取得了最先进的性能。由于其复杂的结构和黑箱性质,对其工作机制和决策过程的解释至关重要,尤其是在医疗保健等敏感领域。可视分析(VA)结合 DL 方法已被广泛用于发现数据洞察力,但它们经常会遇到视觉杂波(VC)问题。本研究提出了一种紧凑型神经网络(NN)视图设计,以减少为领域专家和终端用户解释 DL 模型组件时的视觉杂乱。我们利用聚类算法,根据激活的相似性对隐藏神经元进行分组。这种设计支持神经元簇的整体和细节视图。我们使用了一个表格式的医疗保健数据集作为案例研究。聚类结果的设计将神经元表征之间的视觉杂乱度降低了 54%,将神经元之间的连接降低了 88.7%,并有助于观察在训练过程中学到的类似神经元激活。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Visual Analytics in Explaining Neural Networks with Neuron Clustering
Deep learning (DL) models have achieved state-of-the-art performance in many domains. The interpretation of their working mechanisms and decision-making process is essential because of their complex structure and black-box nature, especially for sensitive domains such as healthcare. Visual analytics (VA) combined with DL methods have been widely used to discover data insights, but they often encounter visual clutter (VC) issues. This study presents a compact neural network (NN) view design to reduce the visual clutter in explaining the DL model components for domain experts and end users. We utilized clustering algorithms to group hidden neurons based on their activation similarities. This design supports the overall and detailed view of the neuron clusters. We used a tabular healthcare dataset as a case study. The design for clustered results reduced visual clutter among neuron representations by 54% and connections by 88.7% and helped to observe similar neuron activations learned during the training process.
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