Investigating the influence of noise and distractors on the interpretation of neural networks
Investigating the influence of noise and distractors on the interpretation of neural networks
复制标题
研究噪声和干扰因素对神经网络解释的影响
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Sven Dähne
中科院分区：
文献类型：
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作者：
Pieter;Kristof Schütt;K. Müller;Sven Dähne
了解神经网络变得越来越重要。在过去的几年中，已经提出了不同类型的可视化和解释方法。但是，在存在噪音和分散注意力元素的情况下，没有一个明确考虑这种行为。在这项工作中，我们将展示噪声和分心的维度如何影响解释模型的结果。这提供了一个新的理论见解，以帮助选择深泰勒分解框架中最合适的解释模型。
Understanding neural networks is becoming increasingly important. Over the last few years different types of visualisation and explanation methods have been proposed. However, none of them explicitly considered the behaviour in the presence of noise and distracting elements. In this work, we will show how noise and distracting dimensions can influence the result of an explanation model. This gives a new theoretical insights to aid selection of the most appropriate explanation model within the deep-Taylor decomposition framework.