论文标题
测量和提高视觉解释的质量
Measuring and improving the quality of visual explanations
论文作者
论文摘要
解释神经网络决策的能力与他们的安全部署息息相关。已经提出了几种方法，以突出显示对给定网络决策很重要的功能。但是，如何衡量这些方法的有效性尚无共识。我们提出了一种评估解释的新程序。我们使用它来研究从神经网络中的一系列可能来源提取的视觉解释。我们量化了结合这些来源的好处，并挑战了考虑偏见参数的最新呼吁。我们通过一般评估偏差参数在成像网分类器中的影响来支持我们的结论
The ability of to explain neural network decisions goes hand in hand with their safe deployment. Several methods have been proposed to highlight features important for a given network decision. However, there is no consensus on how to measure effectiveness of these methods. We propose a new procedure for evaluating explanations. We use it to investigate visual explanations extracted from a range of possible sources in a neural network. We quantify the benefit of combining these sources and challenge a recent appeal for taking bias parameters into account. We support our conclusions with a general assessment of the impact of bias parameters in ImageNet classifiers
