论文标题
深度神经网络的层次相关性可解释性的概括
Generalization on the Enhancement of Layerwise Relevance Interpretability of Deep Neural Network
论文作者
论文摘要
深度神经网络的实际应用仍然受其缺乏透明度的限制。为人工智能（AI）做出的决定提供解释的努力之一是使用显着性或热图突出了相关区域，这些区域对其预测产生了重大贡献。先前引入了一种层次振幅过滤方法，以提高热图的质量，并通过噪声尖峰抑制进行误差校正。在这项研究中，我们通过考虑任何可识别的错误并假设存在可解释的信息来概括层误差校正。研究了通过层次相关方法传播的错误形式，我们提出了一种过滤技术，以使其可解释性信号的矫正对所使用的特定神经网络的信号幅度趋势进行tailly。最后，我们提出了使用可解释信息的论点。
The practical application of deep neural networks are still limited by their lack of transparency. One of the efforts to provide explanation for decisions made by artificial intelligence (AI) is the use of saliency or heat maps highlighting relevant regions that contribute significantly to its prediction. A layer-wise amplitude filtering method was previously introduced to improve the quality of heatmaps, performing error corrections by noise-spike suppression. In this study, we generalize the layerwise error correction by considering any identifiable error and assuming there exists a groundtruth interpretable information. The forms of errors propagated through layerwise relevance methods are studied and we propose a filtering technique for interpretability signal rectification taylored to the trend of signal amplitude of the particular neural network used. Finally, we put forth arguments for the use of groundtruth interpretable information.
